Microsoft Word - 7444.docx 3791 Copyright © 2025 The Author IDEAS is licensed under CC-BY-SA 4.0 License Issued by English study program of IAIN Palopo IDEAS Journal of Language Teaching and Learning, Linguistics and Literature ISSN 2338-4778 (Print) ISSN 2548-4192 (Online) Volume 13, Number 2, December 2025 pp. 3791 - 3804 The Translation of Introduction Part of Academy’s Genius Swordmaster’s Comic Using Machine Translation Arya Nurusy Syifa 1, Ruminda 2, Ice Sariyati 3 syifa.arya24@gmail.com 1,2,3 Sastra Inggris, ADHUM UIN SGD Bandung Received: 2025-07-08 Accepted: 2025-07-31 DOI: 10.24256/ideas.v13i2.7444 Abstract This study examined the accuracy and clarity of machine translation in the context of fictional texts, with a specific focus on the comic Academy’s Genius Swordmaster. Using Google Translate as the primary tool, a qualitative analysis was conducted to evaluate how effectively the machine translated the comic’s content. The results revealed that 63% of the translated text contained errors, with many issues related to accuracy such as overly literal translations, incorrect word choices, and loss of intended meaning. Problems with clarity were also frequent, as many sentences sounded unnatural, too formal, or awkward in Indonesian, disrupting the flow and making them harder to read. These shortcomings were particularly noticeable in emotional storytelling, where tone, character expression, and cultural nuance play a key role in delivering the intended message. The findings highlight that despite recent advancements, current MT systems still face significant challenges in rendering creative and context-sensitive texts. Future research could expand this work by testing multiple MT systems or exploring post-editing approaches to improve translation quality in fictional narratives. Keywords: Accuracy, Clarity, Comic, Translation, Machine Translation Introduction In recent years, the way people communicate across languages has changed dramatically, largely due to the rapid growth of translation technology. Language barriers, once a major obstacle in education, entertainment, and everyday interaction, are now easier to overcome thanks to widely available translation tools. Among these innovations, Machine Translation (MT) has become one of the most prominent, offering users the ability to translate text quickly and efficiently, even with little or no knowledge of foreign languages. The rapid development of Arya Nurusy Syifa, Ruminda, Ice Sariyati The Translation of Introduction Part of Academy’s Genius Swordmaster’s Comic Using Machine Translation 3792 technology has brought many conveniences to daily life, including the field of translation. With innovation advancing at an unprecedented pace, tools and services once considered unimaginable are now widely accessible. As Kirchhoff, Turner, and Axelrod (2016) observe, the widespread accessibility of MT systems has significantly expanded language access for non-expert users across both formal and informal contexts. Tools such as Google Translate are valued because they work quickly across multiple platforms, allowing users to translate sentences, documents, or even spoken input in seconds. However, as Munday (2016) points out, translation is more than simply converting words; it requires understanding meaning, cultural context, and communicative intent—elements that automated systems often struggle to capture. Despite their popularity and ongoing development, MT systems face persistent challenges. The translations produced are not always accurate and may at times sound unnatural or confusing, especially when dealing with complex sentences, idiomatic expressions, or context-specific language. Such inaccuracies can lead to misunderstandings or misinterpretations. Koehn and Knowles (2017) note that rare vocabulary, long sentences, and mismatched subject matter continue to affect translation quality, while Wan et al. (2022) highlight the difficulties of translating short texts, where the lack of context frequently leads to errors. MT also struggles with maintaining tone and nuance in formal or highly contextual texts. Zhang and Zong (2020) emphasize that neural MT systems rely heavily on large, high-quality datasets, making it difficult to produce accurate translations for low-resource languages or nuanced content where tone and context are not explicitly indicated. Fictional or creative works reveal these weaknesses most clearly, as they demand not only accuracy but also sensitivity to tone, humor, and cultural nuance (O’Hagan, 2016; Toral & Way, 2018). Among fictional works, comics pose unique difficulties for MT due to their reliance on a combination of visual storytelling, creative language, and cultural references. As McCloud (1993) explains, comics operate through a dynamic interplay between words and images, making their interpretation—and, by extension, their translation—highly context-dependent. Elements such as word choice, humor, tone, and cultural references are central to storytelling and character development; if they are mistranslated, the narrative impact and reader experience can be significantly altered. While Guerberof-Arenas and Toral (2022) have examined MT in literary texts, focusing on short stories translated by neural MT systems trained on literary data, they found that the outputs were often literal and lacked creativity compared to human translation. However, previous studies have concentrated on literary prose, leaving a clear gap in research on how MT performs in comics, which differ from other literary forms in their use of short dialogue, visual elements, and culturally specific humor. As Jiménez-Crespo (2017) notes, fictional texts like comics require heightened sensitivity to cultural and narrative nuance, which current MT systems are not yet fully capable of replicating. Addressing this overlooked area is crucial for IDEAS, Vol. 13, No. 2, December 2025 ISSN 2338-4778 (Print) ISSN 2548-4192 (Online) 3793 understanding MT’s limitations in handling fictional, highly contextual language. In response to this gap, the present research focuses on evaluating translations of the comic Academy’s Genius Swordmaster produced by Google Translate, with particular attention to the accuracy and clarity of meaning in the results. Comics, with their creative expressions and culturally embedded humor, provide a valuable testing ground for assessing MT’s ability to preserve meaning and tone in fictional narratives. To guide this investigation, the study is driven by the following questions: 1. How is translation accuracy preserved in texts processed by machine translation? 2. How is translation clarity delivered in texts produced by machine translation? The findings of this study are expected to contribute to a deeper understanding of how MT handles the linguistic, cultural, and contextual challenges of comics. By focusing on the accuracy and clarity of meaning, the research aims to shed light on both the strengths and limitations of MT in translating fictional works. Ultimately, this study seeks to inform both users and developers of translation technologies, offering insights into how MT might be improved to better handle creative and context-dependent narratives in the future. Method This research used a qualitative method to analyze the accuracy and clarity of machine translation in comic texts. A qualitative approach focuses on describing and interpreting meaning rather than relying on numbers or statistical analysis. As Creswell and Poth (2018) explain, qualitative research is commonly used to explore how people make sense of meaning in real-life contexts, particularly through close textual or discourse analysis. In the field of translation studies, Chesterman (2017) notes that qualitative approaches are especially useful for examining how language functions within specific cultural or narrative contexts. Similarly, Saldanha and O’Brien (2013) emphasize that qualitative methods are well suited for analyzing how translated language conveys or alters meaning—especially in creative or context-rich texts. The comic used in this study is Academy’s Genius Swordmaster, and the data were taken from the introduction part. This comic features a variety of character tones, cultural expressions, and emotionally loaded dialogue. Elements that are often difficult for machine translation to handle accurately , and the introduction was selected because it presents the setting, introduces the main character, and establishes the tone of the story. The original English text taken from the introduction part of the comic Academy’s Genius Swordmaster was translated into Indonesian using Google Translate. The dataset included two chapters worth of dialogue from within the comic, which then organized to ensure that no dialogue was cut off. The translation was done without any manual edits, so the result could reflect the tool’s actual performance. The translated version was then compared to the original to see if the meaning was delivered clearly and accurately. Arya Nurusy Syifa, Ruminda, Ice Sariyati The Translation of Introduction Part of Academy’s Genius Swordmaster’s Comic Using Machine Translation 3794 The evaluation was based on a bilingual assessment. As a bilingual reader fluent in both English and Indonesian, the researcher analyzed the translation with a primary focus on clarity and accuracy, supported by subcategories including naturalness and preservation to the original meaning, using a structured error classification to ensure consistent analysis. As an example, The part “-der!” is a cut- off from the word “Commander,” but the machine translation could not recognize or complete it, leaving it not translated. Or something like “That’s good” got translated into “Itu bagus”, it is correct but lacks the nuance from the original sentence. As Newmark (1988) points out, a translation works best when it keeps the message true to the original while making sure it’s easy to read and understand in the new language. Nida and Taber (1969) also explain that a translation should have the same effect on the reader as the original, which sometimes means changing how things are said so they make sense culturally and emotionally. Sections that felt awkward, unclear, or inaccurate were noted and discussed further. Extra attention was given to expressions with emotion, informal phrases, or story- related context that might be difficult for machine translation to handle. As because comics combine both text and images, this study also looked at how the translated dialogue fit with the visuals. The character expressions, scenes, and overall panel context were considered to check whether the translation still made sense and matched the visual storytelling. This part helped to understand how well Machine Translation worked with comics, which rely heavily on both written and visual meaning. By focusing on one comic and one MT tool, this study aims to give a better understanding of how machine translation performs with creative and informal content. The findings are expected to show how reliable Machine Translation is when used for comic translation from English into Indonesian, in this case Google Translate. Table 1: Data collections table No Original (English) Target (Indonesian) 1. ha,,, so many of them died damn it. ha,,, begitu banyak dari mereka yang mati sialan. 2. ahaiyute’s clear carelessness, to think there was such a strong human. kecerobohan jelas ahaiyute, berpikir ada manusia sekuat itu 3. you bastard, you are still alive? kau brengsek, kau masih hidup? 4. the giant ahaiyute, after he showed himself in the empire 20 raksasa ahaiyute, setelah dia menunjukkan dirinya di kerajaan 20 IDEAS, Vol. 13, No. 2, December 2025 ISSN 2338-4778 (Print) ISSN 2548-4192 (Online) 3795 days ago, he wreaked havoc for unknown reasons, he was the last of the three giants. hari yang lalu, dia menimbulkan kehancuran tanpa alasan yang jelas, dia adalah yang terakhir dari tiga raksasa. 5. because of you, half of the empire became a cemetery, but why are you still alive? karena kamu, setengah dari kerajaan menjadi kuburan, tetapi mengapa kamu masih hidup? 6. don't worry, since i wont be for much longer, ahaiyute will soon return to “his” side. jangan khawatir, karena saya tidak akan lama lagi, ahaiyute akan segera kembali ke 'sisinya'. 7. That's good. since your friends, who died before you, will be waiting. Itu bagus. Karena teman-temanmu, yang telah meninggal sebelum kamu, akan menunggu. 8. friends? Yeah, the other two who came with you. One became a fried giant because of a hot- tempered red dragon, and the other one was sealed for eternity by an old man called lorehon. Do you get it, you damned bastards? teman? Ya, dua orang lain yang datang bersamamu. Satu menjadi raksasa yang terpanggang karena naga merah yang pemarah, dan yang lainnya disegel selamanya oleh seorang pria tua bernama Lorehon. Apakah kamu mengerti, kau bangsat terkutuk? 9. don't cover up the truth with superficial words, strong one. the children of the star share their senses with one another. We know that their opponents have been annihilated along with them. and, it means that strong people like you no longer exist here. jangan menutupi kebenaran dengan kata-kata dangkal, orang yang kuat. anak-anak bintang berbagi panca indera mereka satu sama lain. Kami tahu bahwa lawan mereka telah dihancurkan bersamaan dengan mereka. dan, itu berarti orang-orang kuat seperti kamu tidak lagi ada di sini. 10. hah! I'm still alive, so what the hell are you talking about? Hah! Saya masih hidup, jadi apa yang kamu bicarakan? 11. don't get ahead of yourself. I know that your time is reaching its end. I'm quite glad. if you had realized your skill earlier and put your all into training, you jangan terlalu terburu-buru. Saya tahu bahwa waktu Anda hampir habis. Saya cukup senang. Jika kamu menyadari bakatmu lebih awal dan mengerahkan semua usaha dalam Arya Nurusy Syifa, Ruminda, Ice Sariyati The Translation of Introduction Part of Academy’s Genius Swordmaster’s Comic Using Machine Translation 3796 would’ve become a large obstacle for our long-cherished wish since just now, this guy pelatihan, kamu pasti akan menjadi rintangan besar bagi keinginan kami yang telah lama kami impikan sejak tadi, orang ini. 12. fine. even if you hadn’t said anything, i know i’ve lived a fucked-up life. so shut up and fucking die. you’re a great human. It's alright to be proud. but, in the end, your world will soon be engulfed by the starlight and disappear halus. Bahkan jika Anda tidak mengatakan apa-apa, saya tahu saya telah menjalani kehidupan yang kacau balau. jadi diam dan mati. Anda adalah manusia yang hebat. Tidak apa-apa untuk bangga. Tapi, pada akhirnya, dunia Anda akan segera ditelan oleh cahaya bintang dan menghilang. 13. What do you want me to do about the things that have already happened Apa yang kamu ingin saya lakukan tentang hal-hal yang sudah terjadi? 14. Were you all here? you foolish bastards What kind of pride made you fight until you died? Do you think you all were strong because I was? you should’ve read the room and run away till the end so foolish just why? Apakah kalian semua di sini? kalian bajingan bodoh. Jenis kebanggaan apa yang membuat kalian bertarung sampai mati? Apakah kalian pikir kalian semua kuat karena saya? seharusnya kalian bisa membaca keadaan dan lari sampai akhir begitu bodoh mengapa? 15. on the giants, who could deflect the empire’s strongest sword aura, why was my sword the only one that worked? if it weren’t for that, there would be no way a disciplinary soldier would stand in the front lines, and you guys wouldn’t have died first? I really don’t know tentang raksasa, yang bisa membelokkan aura pedang terkuat dari kekaisaran, mengapa hanya pedang saya yang berhasil? Kalau bukan karena itu, tidak mungkin seorang prajurit disipliner berdiri di garis depan, dan kalian tidak akan mati lebih dulu? Saya benar-benar tidak tahu. 16. anyone there ? I can't move because of my injuries. th-this voice! you were, anyone, you were still alive! so you were still alive! Grand commander! ada siapa di sana? Aku tidak bisa bergerak karena luka-lukaku. Su- suara ini! Kamu, siapa pun, kamu masih hidup! jadi kamu masih hidup! Panglima besar! Adeshan, IDEAS, Vol. 13, No. 2, December 2025 ISSN 2338-4778 (Print) ISSN 2548-4192 (Online) 3797 Adeshan, 17. for someone who’s in command, what’s this? ronan aren't you a corporal? you aren't that great yourself. more importantly, is ahaiyute dead? Yes, I killed him. his body isn't too far from here. I see he's dead. Thank you, Corporal ronan. untuk seseorang yang memimpin, apa ini? ronan bukankah kau seorang kopral? kau juga tidak begitu hebat sendiri. yang lebih penting, apakah ahaiyute sudah mati? Ya, aku membunuhnya. tubuhnya tidak terlalu jauh dari sini. Aku lihat dia sudah mati. Terima kasih, Kopral ronan 18. ah, if you're that grateful, would you listen to my request? a request? the clean up of the disciplinary soldiers. Usually, as per the regulation, they are abandoned or burned but could you treat them well this time? Ah, jika kamu begitu bersyukur, maukah kamu mendengarkan permintaanku? Permintaan? Pembersihan para prajurit disiplin. Biasanya, menurut peraturan, mereka ditinggalkan atau dibakar, tetapi bisakah kamu memperlakukan mereka dengan baik kali ini? 19. I'm not sure you're more alive than me, so wouldn’t it be better if you personally looked after them? gosh how could I, commander cough!! cough, cough!! ugh see What do you mean, more alive? I could die any second I was fooling around most days, so I couldn't do much, so it feels unfair that I'm going to die Saya tidak yakin Anda lebih hidup daripada saya, jadi bukankah lebih baik jika Anda merawat mereka secara pribadi? astaga, bagaimana bisa saya, komandan batuk!! batuk, batuk!! ugh lihat, apa maksudmu, lebih hidup? Saya bisa mati kapan saja, saya biasa bermain-main hampir setiap hari, jadi saya tidak bisa banyak berbuat, jadi rasanya tidak adil bahwa saya akan mati. 20. If you survive, is there something you want to do? I have a lot of things I want to do~ but the only thing I can think of right now, what would it be like to go to the academy? the academy? Jika kamu selamat, apakah ada sesuatu yang ingin kamu lakukan? Saya memiliki banyak hal yang ingin saya lakukan~ tetapi satu-satunya hal yang bisa saya pikirkan saat ini, bagaimana rasanya pergi ke akademi? Akademi? 21. well the shit that giant bastard said is bothering me but more nah, omongan sialan yang diucapkan raksasa itu menggangguku, tapi yang Arya Nurusy Syifa, Ruminda, Ice Sariyati The Translation of Introduction Part of Academy’s Genius Swordmaster’s Comic Using Machine Translation 3798 importantly my late sister’s wish was to send me to the academy but soon I'll be reunited with my family when the rain stops, the search party will ah I already told you I'm a lost cause, so why do you keep saying useless things lebih penting, keinginan mendiang saudariku adalah mengirimku ke akademi, tetapi segera aku akan bersatu kembali dengan keluargaku ketika hujan berhenti, tim pencari akan ah aku sudah bilang padamu, aku adalah masalah yang hilang, jadi mengapa kau terus mengatakan hal- hal yang tidak berguna. 22. what? the rain stopped? the storm suddenly stopped? woah!! wh- what?!!! eight wings?! What the hell, these guys?! where did they come crawling out from?!! Apa? Hujan berhenti? Badai tiba-tiba berhenti? Woah!! Wh- apa?!!! Delapan sayap?! Apa-apaan ini, orang-orang ini?! Dari mana mereka muncul?!! 23. No way the three giants weren’t the end of it? In the end I couldn't save this world. a third time? with only one last chance left I can't think of a way Tidak mungkin ketiga raksasa itu bukan yang terakhir? Pada akhirnya, saya tidak bisa menyelamatkan dunia ini. Untuk ketiga kalinya? Dengan hanya satu kesempatan terakhir yang tersisa, saya tidak bisa memikirkan cara. 24. -der! commander! Grand commander-!!! heok?! What are you doing? get a hold of yourself! Hurry up and use telekinesis or anything to shoot me upwards?! ah fuck!! my sword can't reach them from here! Der! Komandan! Komandan besar-!!! Heok?! Apa yang kau lakukan? Kendalikan dirimu! Cepat gunakan telekinesis atau apapun untuk mengangkatku?! Ah sial!! Pedangku tidak bisa menjangkau mereka dari sini! 25. wh-what? are you not going to kill them?! y-you're going to fight in this situation? wh-apa? apakah kamu tidak akan membunuh mereka?! y-kamu akan bertarung dalam situasi ini? 26. of course!! what, you want to just sit still and kick the bucket? Even if I die, shouldn't I give some shit to those bastards?!!! Tentu saja!! Apa, kamu ingin hanya duduk diam dan menunggu mati? Bahkan jika saya mati, bukankah saya seharusnya memperlakukan bajingan-bajingan itu dengan keras?!!! 27. He's not giving up in this Dia tidak menyerah dalam situasi ini? IDEAS, Vol. 13, No. 2, December 2025 ISSN 2338-4778 (Print) ISSN 2548-4192 (Online) 3799 situation? yeah, he's such a man that's why we were able to get to this stage. if it's him corporal ronan. ah, what?!! i'm busy so stop talking -?! What what?! Ya, dia memang pria seperti itu, itulah sebabnya kami bisa mencapai tahap ini. Jika dia, Kopral Ronan. Ah, apa?!! Saya sibuk, jadi berhenti bicara -?! Apa?! 28. What did you put in my mouth? what? Wait, what are you? What the hell are you saying, in this situation?!! Apa yang kamu masukkan ke mulutku? Apa? Tunggu, kamu ini apa? Apa yang kamu katakan, dalam situasi ini?!! 29. corporal ronan if we meet again can you tell me to not do anything stupid and become a tailor? I've done a lot of things but nothing quite like that. Korporal Ronan, jika kita bertemu lagi, bisakah kamu memberitahuku untuk tidak melakukan hal-hal bodoh dan menjadi penjahit? Saya sudah melakukan banyak hal, tetapi tidak ada yang seperti itu. 30. heok?! !! grand grand commander!! grand comman -der im- impossible this place could it be my hometown, “nimbuten”? did i really return to the past? special note day 1 of regression. heok?! !! komandan besar!! komandan besar -der im- tidak mungkin tempat ini bisa jadi kampung halamanku, “nimbuten”? apa aku benar-benar kembali ke masa lalu? catatan khusus hari 1 regresi. The writer examined the data through the following process: 1. Reading the introduction part of the comic 2. Analyzing the translated comic 3. Making notes about the error of the machine translation found 4. Classifying the error 5. Explaining the data 6. Making the conclusion Results The analysis found several issues related to accuracy and clarity in the Indonesian translation of the comic’s introduction. Although some sentences were understandable, around 63% of it showed awkward wording, unnatural flow, and meaning that did not fully match the original. In several cases, the translation felt stiff or confusing, especially in emotional or dramatic scenes. Arya Nurusy Syifa, Ruminda, Ice Sariyati The Translation of Introduction Part of Academy’s Genius Swordmaster’s Comic Using Machine Translation 3800 Table 2: Frequency of error in accuracy and clarity. Error type Description Frequency Percentage Accuracy Some translated words were too literal, had incorrect word choices, left out cut-off words, or failed to convey the intended expressions. 10 53% Clarity Many lines felt unnatural, with a tone that was too formal and stiff 9 47% Total 19 100% In terms of accuracy, the translation showed several problems. Some words or phrases were translated too literally, causing the meaning to shift or become unclear. Expressions, inner thoughts, or story-specific phrases were often changed into awkward or incorrect meanings. There were also lines where the main message was not clearly delivered, which made the sentence feel disconnected from the original context. In emotional scenes, the Indonesian version sometimes gave a weaker or different impression from the original, which affected how the reader experienced the story. Clarity was also a major issue throughout the translation. Many sentences sounded unnatural or stiff, like they were directly copied from English grammar. This made the flow feel off in Indonesian. Some dialogue lines were too formal, while others were casual, even if spoken by the same character. This inconsistency made the characters’ voices feel uneven. In certain panels, the translated dialogue didn’t match the scene or the character expressions, making it harder for readers to follow the story smoothly. The following data present the identified Accuracy issues, subcategorized and explained to show which parts of the translation were problematic: 1. Too Literal With the translated result produced seems overly literal so it failed to capture the intended meaning. So, making the translation technically correct but not accurate in context matters. Data 6. Each word was translated literally, losing the intended means from the original sentences. Data 17. This one also showcases the same problem, making the result lose its intended meaning it tries to convey. Data 30. With the literal translation of the sentences the conveyed meaning also lost. IDEAS, Vol. 13, No. 2, December 2025 ISSN 2338-4778 (Print) ISSN 2548-4192 (Online) 3801 2. Expression In this translation, the result was wrongly expressed as the original meaning of the text got disoriented. As an effect to the reading experience, it might lead to misunderstanding. Data 12. The words “fine” translated wrongly, as it was meant to be a form of expression but instead the MT translated it as “halus” making it not right in conveying expression. Data 26. In this case the words “shouldn't I give some shit to those bastards” translated into “bukankah saya seharusnya memperlakukan bajingan- bajingan itu dengan keras”. This is a wrong type of expression that the words were trying to convey, losing its originally intended meaning. 3. Cut Off Word This data shows that MT cannot translate the sentences that got cut off as it does not know the context of the words. So, the machine decided to keep the original. Making it lose the feel from the words. Data 22. The “wh-” got left out as is, so the nuance of the sentence was lost. Data 24. In this case the part “-der!” is a cut off word from ‘Commander’, but MT decided it did not know how to proceed with the translation so it just left it out as is. Data 25. Both words “Wh-” and “y-” are kept by the MT as it does not know the original words. So, the reading experience got interrupted. Data 30. “-der im-” this also got left out by the MT showing that it does not know what to translate it to. 4. Wordschoise In this data case the translation shows a decent result, but the word chosen is not accurate in the context of the text. And so, the meaning got distorted for the reader. Data 22. The sentence “where did they come crawling out from” translated into “Dari mana mereka muncul”, there are some words that did not get translated properly so the nuance from the original sentence got lost in the translated one. And here are the following data present the identified Clarity issues, subcategorized and explained to show which parts of the translation were problematic: 1. Unnatural This data showed the translated results are correctly translated to the targeted language but it sounds unnatural, making it apparent that it was not produced by humans. Data 2. The translated result does not sound natural. Losing the nuance of the sentence. Data 9. The same thing also happened to this translation. Losing its initial nuance. Arya Nurusy Syifa, Ruminda, Ice Sariyati The Translation of Introduction Part of Academy’s Genius Swordmaster’s Comic Using Machine Translation 3802 Data 19. With a long sentence, it also opposes the same problem. It does not flow naturally, losing its nuance. Data 29. The first part was almost good but at the end it began to lose its natural flow. Again, losing the intended nuance. 2. Too formal For this data case the translation is too formal, and so contextually it did not match the nuance of the story. Contradicted with the cursed word present in the sentence itself. Data 3, 8, 11, and 15. All these translation results produced a formal tone making it not compatible with the intended nuance, so you lost the feeling in reading it. 3. Too stiff For this data it feels too stiff or rigid, lacking the natural rhythm and fluidity of everyday language, which makes the text sound mechanical and less engaging. Data 7. Showing too much rigidity makes it awkward to read even understanding it. Losing the nuance of everyday or light conversational feeling to it. Lastly, the emotional tone of the text was not fully delivered in the translation. Strong or dramatic lines meant to convey fear, anger, or determination often came across as flat or emotionless in Indonesian, weakening the intended impact to the reading experience. Because comics rely heavily on both language and visuals to draw readers into the story, this loss of emotional depth disrupted the reading experience and reduced the overall engagement making scenes feel less immersive and characters less relatable. As a result, key moments that were meant to evoke strong reactions from readers often failed to do so, leaving the narrative feeling less engaging and emotionally distant. These issues highlight that Machine Translation, in this case Google Translate, still struggles with context, tone, and language style in creative texts like comics. Discussion The results of this research showed several important points about the use of Google Translate in comic translation. First, there were many issues with accuracy. Some phrases were translated too literally, certain expressions lost their original meaning, and in a few cases, the translation even gave a different impression compared to the English version. These problems mostly appeared in parts with strong emotions, inner thoughts, or informal expressions, and the choice of words often did not fit the story’s context. Second, clarity was also a major problem. Many sentences sounded awkward or unnatural in Indonesian, with grammar that followed English too closely and made the text difficult to read. The tone was inconsistent—some lines felt formal while others were casual, even within the same scene—and in several panels, the dialogue did not match the characters’ expressions or situations, leaving the scenes disconnected. These findings show IDEAS, Vol. 13, No. 2, December 2025 ISSN 2338-4778 (Print) ISSN 2548-4192 (Online) 3803 that Google Translate still struggles to deliver translations that are accurate, clear, and true to the original tone and style of the comic. These findings have significant implications. For translators, they emphasize the importance of careful revision and contextual understanding when working with machine-translated texts. Comics often rely on subtle cues in language to express humor, tension, or character personality; when these are lost, the story’s impact diminishes. Translators must therefore go beyond literal correction and focus on restoring nuance and emotional depth to preserve the intended reading experience. For comic readers, these shortcomings can lead to confusion, misinterpretation of character intentions, and a loss of immersion in the story. Awkward phrasing or inaccurate dialogue can make characters feel flat or disconnected from the visuals, weakening the overall engagement and enjoyment of the comic. This research found that Google Translate often struggled with accuracy and clarity when translating the introduction of Academy’s Genius Swordmaster into Indonesian, leading to literal phrasing, awkward wording, and a loss of emotion. Similar issues were reported by Guerberof-Arenas and Toral (2022), who noted that machine translation of Kurt Vonnegut’s 2 BR 0 2 B often missed stylistic and emotional nuances. While their study focused on literary prose and this one on comics, both show that MT still has difficulty handling creative texts that depend on tone and expression. This study has a few limitations that need to be mentioned. The analysis only looked at one comic, Academy’s Genius Swordmaster, and used Google Translate as the only machine translation tool. Because of this, the results may not fully reflect how other comics with different styles and language use would turn out, or how other MT tools might handle translations. The study also did not include post- editing, which could have helped improve many of the problems found in the results. Future studies could build on this by looking at more comics across different genres, trying out different MT systems, and exploring the use of post- editing to improve accuracy, clarity, and emotional tone, giving a clearer picture of how MT works in creative translation. Conclusion This research explored how Google Translate performs in translating the introduction part of the comic Academy’s Genius Swordmaster from English into Indonesian. The results showed that while the translation was somewhat understandable, it had many issues—especially with accuracy and clarity. In terms of accuracy, some meanings were changed due to overly literal translations, and certain phrases didn’t fully reflect the original intent. For clarity, many of the sentences sounded awkward, stiff, or unnatural in Indonesian, which made the dialogue harder to follow. The emotional tone and style of the original text were also not delivered well. These problems became more noticeable in scenes with strong emotions or Arya Nurusy Syifa, Ruminda, Ice Sariyati The Translation of Introduction Part of Academy’s Genius Swordmaster’s Comic Using Machine Translation 3804 informal expressions. The tone often felt inconsistent, and the translated lines sometimes didn’t match the visuals or character expressions. These findings show that machine translation, in its current form, still struggles to handle creative content like comics. It lacks the ability to fully understand context, adjust tone, or deliver meaning naturally across languages, especially in texts where style and emotion are important parts of the message. References Chesterman, A. (2017). Reflective Translation Studies: Translation as an Ethical and Social Practice. Routledge. https://doi.org/10.4324/9781315645357 Creswell, J. W., & Poth, C. N. (2018). Qualitative Inquiry and Research Design: Choosing Among Five Approaches (4th ed.). SAGE Publications. Guerberof-Arenas, A., & Toral, A. (2022). Creativity in translation: Machine translation as a constraint for literary texts. Target: International Journal of Translation Studies, 34(1), 27–51. https://doi.org/10.1075/target.21025.gue Ismayanti, D., Said, Y. R., Usman, N., & Nur, M. I. (2024). The Students Ability in Translating Newspaper Headlines into English A Case Study. IDEAS: Journal on English Language Teaching and Learning, Linguistics and Literature, 12(1), 108-131. Jiménez-Crespo, M. A. (2017). Translation and Web Localization. Routledge. https://doi.org/10.4324/9781315747326 Kirchhoff, K., Turner, A., & Axelrod, A. (2016). Toward multilingual neural machine translation with universal encoder and decoder. In Proceedings of the 13th Conference of the Association for Machine Translation in the Americas (AMTA). Koehn, P., & Knowles, R. (2017). Six challenges for neural machine translation. Proceedings of the First Workshop on Neural Machine Translation, 28–39. https://doi.org/10.18653/v1/W17-3204 Masruddin, M., & Nasriandi, N. (2022). Lexical and Syntactical Errors Performed by Junior High School Student in Writing Descriptive Text. IDEAS: Journal on English Language Teaching and Learning, Linguistics and Literature, 10(1), 1094-1100. McCloud, S. (1993). Understanding Comics: The Invisible Art. HarperCollins. Munday, J. (2016). Introducing Translation Studies: Theories and Applications (4th ed.). Routledge. https://doi.org/10.4324/9781315692886 Newmark, P. (1988). A Textbook of Translation. Prentice Hall. Nida, E. A., & Taber, C. R. (1969). The Theory and Practice of Translation. Brill. O’Hagan, M. (2016). Massively open translation: Unpacking the relationship between technology and translation in the 21st century. International Journal of Communication, 10, 929–946. https://ijoc.org/index.php/ijoc/article/view/3496 Saldanha, G., & O’Brien, S. (2013). Research Methodologies in Translation Studies. Routledge. https://doi.org/10.4324/9781315760097 Toral, A., & Way, A. (2018). What level of quality can neural machine translation IDEAS, Vol. 13, No. 2, December 2025 ISSN 2338-4778 (Print) ISSN 2548-4192 (Online) 3805 attain on literary text? In Translation Quality Assessment: From Principles to Practice, 263–287. https://doi.org/10.1075/btl.141.15tor Wan, Y., Yang, B., Wong, D. F., Chao, L. S., Yao, L., Zhang, H., & Chen, B. (2022). Challenges of neural machine translation for short texts. Computational Linguistics, 48(2), 321–342. https://doi.org/10.1162/coli_a_00435 Zhang, J., & Zong, C. (2020). Neural machine translation: Challenges, progress and future. Science China Technological Sciences, 63(10), 2028–2050. https://doi.org/10.1007/s11431-020-1632-x