JURNAL RISET INFORMATIKA Vol. 5, No. 1 December 2022 P-ISSN: 2656-1743 |E-ISSN: 2656-1735 DOI: https://doi.org/10.34288/jri.v5i1.471 Accredited rank 3 (SINTA 3), excerpts from the decision of the Minister of RISTEK-BRIN No. 200/M/KPT/2020 111 IMPLEMENTATION OF THE FUZZY TSUKAMOTO METHOD FOR OFFICE STATIONERY ESTIMATION STOCK SYSTEM Tedy Rukmana-1*), Hersanto Fajri-2, Dahlia Widhyaestoeti-3 Fakultas Teknik & Sains, Program Studi Teknik Informatika, Konsetrasi Sistem Informasi, Universitas Ibn Khaldun Bogor, Indonesia https://uika-bogor.ac.id/ tedyrukmana1502@gmail.com-1*), hersanto.fajri@gmail.com-2, dahlia@uika-bogor.ac.id-3 (*) Corresponding Author Abstract Information technology supports the company's operational activities in recording incoming goods, outgoing goods, and existing inventory. PT. Mitraniaga Distribusindo is a company engaged in food distribution, which includes supporting aspects for smooth operations, such as stock availability of office stationery. The Tsukamoto fuzzy method aims to calculate the estimated stock of office stationery needed to provide recommendations for deciding the estimated stock. The estimated target variable is more numerical, with models built using complete records providing the variable value of the target as a predictive value. Testing the accuracy of the calculation results of the office stationery stock estimation system was conducted using the Root Mean Squared Error (RMSE) to get a value of 5.9 to make the decisions more precise. In application development, using the waterfall model, which provides a sequential or sequential software life flow approach starting from design analysis, coding, and up to application testing using black box testing that is produced, each test form produces an appropriate status, according to the expected results. Keywords: Fuzzy Tsukamoto; Root Mean Squared Error; Estimation Stock System Abstrak Teknologi informasi mempunyai peranan penting untuk menunjang seluruh aktifitas operasional perusahaan dalam mencatat barang masuk, barang keluar serta persediaan barang yang ada. PT. Mitraniaga Distribusindo merupakan salah satu perusahan yang bergerak dibidang distribusi makanan yang tentu didalamnya terdapat aspek pendukung untuk kelancaran operasional seperti ketersediaan stok alat tulis kantor. Metode fuzzy tsukamoto bertujuan untuk melakukan perhitungan estimasi stok alat tulis kantor yang dibutuhkan mendatang sehingga mampu memberikan rekomendasi dalam memutuskan jumlah estimasi stock alat tulis kantor. Variabel target estimasi lebih kearah numerik dengan model yang dibangun menggunakan record lengkap menyediakan nilai variabel dari target sebagai nilai prediksi. Uji keakuratan hasil perhitungan sistem estimasi stok alat tulis kantor dilakukan menggunakan Root Mean Squared Error (RMSE) mendapatkan nilai 5,9 sehingga keputusan yang diperoleh lebih tepat. Dalam pengembangan aplikasi menggunakan model waterfall yang menyediakan pendekatan alur hidup perangkat lunak secara sekuensial atau terurut dimulai dari analisis desain , pengkodean dan sampai pengujian aplikasi menggunakan blackbox testing yang dihasilkan setiap form uji menghasilkan status sesuai, sesuai dengan hasil yang di harapkan. Kata kunci: Fuzzy Tsukamoto; Root Mean Squared Error; Sistem Estimasi Stok INTRODUCTION Science and technology are currently developing very rapidly, especially in the field of information technology, and the development of information technology is very beneficial for developments, one of which is in the field of trade (Haryati, 2012). Information technology is important in supporting all company operations to record incoming, outgoing, and inventory of goods such as PT. Mitraniaga Distribusindo is a company engaged in the food distribution sector, which mailto:tedyrukmana1502@gmail.com-1 mailto:hersanto.fajri@gmail.com P-ISSN: 2656-1743 | E-ISSN: 2656-1735 DOI: https://doi.org/10.34288/jri.v5i1.471 JURNAL RISET INFORMATIKA Vol. 5, No. 1 December 2022 Accredited rank 3 (SINTA 3), excerpts from the decision of the Minister of RISTEK-BRIN No. 200/M/KPT/2020 112 includes aspects that support smooth operations, such as the availability of stationery stock. The importance of stock availability is one factor that affects the smooth operation of a company in which administrative needs need special attention, as is the case in determining the need for office stationery stock. The estimation system can provide recommendations for deciding the number of stock receipts using the Tsukamoto fuzzy method to make decisionsore precise and computerized and prevent subjective decision- making (Novianti Puspitasari, Andi Tejawati, & Friendy Prakoso, 2019). Tsukamoto's fuzzy logic reasoning provides a way to understand system performance by assessing system input and output from observations. The research (Hendi Setiawan, 2020) explains that Fuzzy logic is a technique/method used to deal with uncertainties in problems with many answers. If you look at fuzzy logic, it is easy to understand, and fuzzy logic uses basic set theory. Fuzzy logic is a group set representing a certain state in a variable. An application is needed that can simplify performance and solve problems related to inventory processing, and also the method used is the fuzzy logic method. In research conducted by (Prayogi & Santoso, 2018), the Tsukamoto method is an extension of monotone reasoning. In the Tsukamoto method, each consequence of a rule in IF-THEN must be represented by a fuzzy set with a monotonous membership function. As a result, the output of the inference results from each rule is given strictly (crisp) based on the Ξ± predicate (fire strength). The final result is obtained by using a weighted average. Based on the accuracy test results, the error value obtained from the small forecasting results is 0.0607%. In other research (Huda, 2018), Controlling raw material stocks is an activity that a company or agency always carries out with the constraint that often occurs in raw material stocks. When the demand for raw material quantities is not correct, it can result in running out of raw materials, impacting cafe operations. The implementation of Fuzzy Tsukamoto is widely used to predict or predict the future, such as the use of Fuzzy Tsukamoto in predicting the availability of teak wood based on the variables that affect it. The application of Fuzzy Tsukamoto in controlling warehouse stock recommends purchasing goods based on consumer needs using the Tsukamoto method. Office stationery is a supporting factor for smooth operational activities. Office stationery is objected that is used up in the daily work of administrative employees. (M. Ramaddan Julianti, Muhammad Iqbal Dzulhaq, & Ahmad Subroto, 2019) The waterfall model is a system development model for this research. The waterfall model provides a sequential or sequential software life-flow approach starting from analysis, design, coding, and testing. The Tsukamoto fuzzy method aims to calculate the estimated stock needed in the future to provide recommendations for the estimated amount of office stationery stock. The estimated target variable is more numerical, with models built using complete records providing the variable value of the target as a predictive value. Building a web- based estimation system helps determine the estimated amount of office stationery stock needed by implementing the Tsukamoto fuzzy method. The accuracy of the calculation results of the office stationery stock estimation system is tested using Root Mean Squared Error (RMSE). In application development, the waterfall model provides a sequential or sequential software life flow approach starting from design analysis and coding until the application testing using black box testing. Research (Huda, 2018) shows that Requests for the wrong amount of raw materials can result in running out of raw materials, impacting operational disruptions. There needs to be an appropriate raw material management system to fulfil stock availability in the warehouse. The system to be designed uses the Tsukamoto fuzzy method. In testing the accuracy of the system also uses RMSE (Root Mean Square Error). The results of the Tsukamoto fuzzy test on 25 training data with sales parameters, expiration date, demand, and yield 0.78%. Research on implementation of fuzzy Tsukamoto method for office stationery estimation stock system. Designed to calculate the estimated stock needed for the future so that it can provide advice when determining the estimated amount of office stationery stock. RESEARCH METHODS This study uses a qualitative method approach. To obtain data by interviewing parties related to this research. Describe the meaning of research facts through the interview and observation stages of participation and explain the facts that occur in the field (Muhammad Rijal Fadli, 2021). The research methodology can be seen in Figure 1. JURNAL RISET INFORMATIKA Vol. 5, No. 1 December 2022 P-ISSN: 2656-1743 |E-ISSN: 2656-1735 DOI: https://doi.org/10.34288/jri.v5i1.471 Accredited rank 3 (SINTA 3), excerpts from the decision of the Minister of RISTEK-BRIN No. 200/M/KPT/2020 113 Figure 1. Research Methods Time and Place of Research This research was conducted at PT. Mitraniaga Distribusindo starts from August 2022 to October 2022. Research Target / Subject In this research, the target/subject is the admin head, who acts as the person in charge of the operational office stationery stock, and then the interview stages are carried out in the formation of data, variables, and criteria. Procedure Based on the research method described in Figure 1, where the initial stage is analysis. Analysis of business processes begins with observation, interview stages, and the application of literature studies resulting in output in the form of variable data, criterion data, and alternative data, which is then carried out by the fuzzy Tsukamoto algorithm to produce recommendation values. In the next stage, the results of Tsukamoto's fuzzy calculations built a program with the application of object-oriented programming through the Unified Modeling Language (UML) design process to produce use cases, activities, sequences, classes, and deployment diagrams. The next stage is coding in implementing web-based programs using the PHP programming language, writing code using Visual Studio, databases using MySQL, and web browsers using Chrome. Furthermore, at the testing stage, testing the results of estimation calculations uses the Tsukamoto fuzzy method to find out how accurate the stock inventory estimation is. Carried out by Root Mean Squared Error (RMSE) and black box testing provides test results for the suitability of the application with the function or functional capability of the system. This testing phase is carried out to find out how the results of the system design are to be able to find out deficiencies and what things need to be fixed for further development. Data, Instruments, and Data Collection Techniques In this study, the data formed will then be processed for Tsukamoto fuzzy calculations, for the P-ISSN: 2656-1743 | E-ISSN: 2656-1735 DOI: https://doi.org/10.34288/jri.v5i1.471 JURNAL RISET INFORMATIKA Vol. 5, No. 1 December 2022 Accredited rank 3 (SINTA 3), excerpts from the decision of the Minister of RISTEK-BRIN No. 200/M/KPT/2020 114 criteria can be seen in table 1. the formation of fuzzy sets can be seen in table 2. Table 1. Kriteria Data No Variable Statement 1 Permintaan Office stationery expenditure data 2 Stock Office stationery inventory data 3 Estimasi Office stationery stock estimation data Table 2 Fuzzy Function Function Variable Fuzzy set Universal set Domain Input Permintaan Sedikit 10-80 10 s/d 40 Banyak 40 s/d 80 Stock Sedikit 10-80 10 s/d 40 Banyak 40 s/d 80 Output Estimasi Berkurang 10-80 10 s/d 40 Bertambah 40 s/d 80 Alternative data is office stationery data contained in PT. Mitraniaga Distribusindo. The instrument is to interview the Admin head, who is in charge of office stationery operations. In order to obtain data on the criteria variable for implementing fuzzy through the stages of fuzzification, fuzzy inference, and defuzzification. Data analysis technique with Fuzzy Tsukamoto The Tsukamoto fuzzy algorithm then processes the data obtained by forming variables and fuzzy sets beforehand. As an example of the office stationery stock data that will be processed in this case, the variable values obtained consist of requests 60 and stock 47 and the estimated value to be searched for 1. Fuzzification After obtaining the next variable is the fuzzification process of forming curves, sets, and membership functions for each variable. a. Permintaan There are 2 sets : Sedikit and Banyak Figure 2 Permintaan Curve Figure 2 is a process of fuzzification of the permintaan variable, which consists of sets of sedikit and banyak, showing the degree of membership value resulting from the calculation as follows: The membership function : πœ‡π‘ƒπ‘’π‘Ÿπ‘šπ‘–π‘›π‘‘π‘Žπ‘Žπ‘›π‘†π‘’π‘‘π‘–π‘˜π‘–π‘‘ [π‘₯] = { 1, π‘₯ ≀ 10 80βˆ’π‘₯ 70 , 10 ≀ π‘₯ ≀ 80 0, π‘₯ β‰₯ 80 .................................................................................................. (1) πœ‡π‘ƒπ‘’π‘Ÿπ‘šπ‘–π‘›π‘‘π‘Žπ‘Žπ‘›π΅π‘Žπ‘›π‘¦π‘Žπ‘˜ [π‘₯] = { 0, π‘₯ ≀ 10 π‘‹βˆ’10 70 , 10 ≀ π‘₯ ≀ 80 1, π‘₯ β‰₯ 80 .................................................................................................. (2) The value of the degree of membership is obtained : πœ‡π‘ƒπ‘’π‘Ÿπ‘šπ‘–π‘›π‘‘π‘Žπ‘Žπ‘›π‘†π‘’π‘‘π‘–π‘˜π‘–π‘‘ [60] = 80βˆ’60 70 = 20 70 = 0,285714286 πœ‡π‘ƒπ‘’π‘Ÿπ‘šπ‘–π‘›π‘‘π‘Žπ‘Žπ‘›π΅π‘Žπ‘›π‘¦π‘Žπ‘˜ [60] = 60βˆ’10 70 = 50 70 = 0,714285714 b. Stock There are 2 sets : Sedikit and Banyak Figure 3 Stock Curve JURNAL RISET INFORMATIKA Vol. 5, No. 1 December 2022 P-ISSN: 2656-1743 |E-ISSN: 2656-1735 DOI: https://doi.org/10.34288/jri.v5i1.471 Accredited rank 3 (SINTA 3), excerpts from the decision of the Minister of RISTEK-BRIN No. 200/M/KPT/2020 115 Figure 3 is a process of fuzzification of the stock variable, which consists of sets of sedikit and banyak, showing the degree of membership value resulting from the following calculations : The membership function : πœ‡π‘†π‘‘π‘œπ‘π‘˜π‘†π‘’π‘‘π‘–π‘˜π‘–π‘‘ [𝑦] = { 1, 𝑦 ≀ 10 80βˆ’π‘¦ 70 , 10 ≀ 𝑦 ≀ 80 0, 𝑦 β‰₯ 80 (3) πœ‡π‘†π‘‘π‘œπ‘π‘˜π΅π‘Žπ‘›π‘¦π‘Žπ‘˜ [𝑦] = { 0, 𝑦 ≀ 10 π‘¦βˆ’10 70 , 10 ≀ 𝑦 ≀ 80 1, 𝑦 β‰₯ 80 ...... (4) The value of the degree of membership is obtained : πœ‡π‘†π‘‘π‘œπ‘π‘˜π‘†π‘’π‘‘π‘–π‘˜π‘–π‘‘ [47] = 80βˆ’47 70 = 33 70 = 0,471428571 πœ‡π‘†π‘‘π‘œπ‘π‘˜π΅π‘Žπ‘›π‘¦π‘Žπ‘˜ [47] = 47βˆ’10 70 = 37 70 = 0,528571429 c. Estimasi There are 2 sets : Berkurang and Bertambah Figure 4 Estimasi Curve Figure 4 is a process of fuzzification of the estimated variable. The value to be searched for consists of a "berkurang" and "bertambah" set; the final value will show the value that will enter the "berkurang" or "bertambah" set, and the resulting degree of membership value in Figure 4 is generated from the following calculations : πœ‡πΈπ‘ π‘‘π‘–π‘šπ‘Žπ‘ π‘–π΅π‘’π‘Ÿπ‘˜π‘’π‘Ÿπ‘Žπ‘›π‘” [𝑧] = { 1, 𝑧 ≀ 10 80βˆ’π‘§ 70 , 10 ≀ 𝑧 ≀ 80 0, 𝑧 β‰₯ 80 ...................................... (5) πœ‡πΈπ‘ π‘‘π‘–π‘šπ‘Žπ‘ π‘–π΅π‘’π‘Ÿπ‘‘π‘Žπ‘šπ‘π‘Žβ„Ž [𝑧] = { 0, 𝑧 ≀ 10 π‘§βˆ’10 70 , 10 ≀ 𝑧 ≀ 80 1, 𝑧 β‰₯ 80 ...................................... (6) 2. Fuzzy Inference This stage is the process of forming the rules (rules) that will be used, and the following are the rules in this case : a. [R1] IF Permintaan Sedikit AND Stock Banyak THEN Estimasi Berkurang. b. [R2] IF Permintaan Sedikit AND Stock Sedikit THEN Estimasi Berkurang. c. [R3] IF Permintaan Banyak AND Stock Sedikit THEN Estimasi Bertambah. d. [R4] IF Permintaan Banyak AND Stock Banyak THEN Estimasi Bertambah. The value obtained in the rule ; [R1] IF Permintaan Sedikit AND Stock Banyak THEN Estimasi Berkurang. 𝛼 βˆ’ π‘π‘Ÿπ‘’π‘‘π‘–π‘˜π‘Žπ‘‘1 = πœ‡π‘ƒπ‘’π‘Ÿπ‘šπ‘–π‘›π‘‘π‘Žπ‘Žπ‘›π‘†π‘’π‘‘π‘–π‘˜π‘‘ ∩ πœ‡π‘†π‘‘π‘œπ‘π‘˜π΅π‘Žπ‘›π‘¦π‘Žπ‘˜ = π‘šπ‘–π‘› (0,285714286; 0,714285714) = (0,285714286) 80 βˆ’π‘ 70 = 0,285714 𝑍1 = 60 [R2] IF Permintaan Sedikit AND Stock Sedikit THEN Estimasi Berkurang. 𝛼 βˆ’ π‘π‘Ÿπ‘’π‘‘π‘–π‘˜π‘Žπ‘‘2 = πœ‡π‘ƒπ‘’π‘Ÿπ‘šπ‘–π‘›π‘‘π‘Žπ‘Žπ‘›π‘†π‘’π‘‘π‘–π‘˜π‘‘ ∩ πœ‡π‘†π‘‘π‘œπ‘π‘˜π‘†π‘’π‘‘π‘–π‘˜π‘–π‘‘ = π‘šπ‘–π‘› (0,285714286 ; 0,471428571) = (0,285714286) 80 βˆ’ 𝑍 70 = 0,285714286 𝑍2 = 60 [R3] IF Permintaan Banyak AND Stock Sedikit THEN Estimasi Bertambah. 𝛼 βˆ’ π‘π‘Ÿπ‘’π‘‘π‘–π‘˜π‘Žπ‘‘3 = πœ‡π‘ƒπ‘’π‘Ÿπ‘šπ‘–π‘›π‘‘π‘Žπ‘Žπ‘›π΅π‘Žπ‘›π‘¦π‘Žπ‘˜ ∩ πœ‡π‘†π‘‘π‘œπ‘π‘˜π‘†π‘’π‘‘π‘–π‘˜π‘–π‘‘ = π‘šπ‘–π‘› (0,714285714 ; 0,471428571) = (0,471428571) 𝑍 βˆ’10 70 = 0,471428571 𝑍3 = 43 [R4] IF Permintaan Banyak AND Stock Banyak THEN Estimasi Bertambah. 𝛼 βˆ’ π‘π‘Ÿπ‘’π‘‘π‘–π‘˜π‘Žπ‘‘4 = πœ‡π‘ƒπ‘’π‘Ÿπ‘šπ‘–π‘›π‘‘π‘Žπ‘Žπ‘›π΅π‘Žπ‘›π‘¦π‘Žπ‘˜ ∩ πœ‡π‘†π‘‘π‘œπ‘π‘˜π΅π‘Žπ‘›π‘¦π‘Žπ‘˜ = π‘šπ‘–π‘› (0,714285714 ; 0,528571429) = (0,528571429) 𝑍 βˆ’10 70 = 0,528571429 𝑍4 = 47 P-ISSN: 2656-1743 | E-ISSN: 2656-1735 DOI: https://doi.org/10.34288/jri.v5i1.471 JURNAL RISET INFORMATIKA Vol. 5, No. 1 December 2022 Accredited rank 3 (SINTA 3), excerpts from the decision of the Minister of RISTEK-BRIN No. 200/M/KPT/2020 116 3. Defuzzification Defuzzification is the final stage of calculation using a weighted average. So that the following values are obtained : 𝑧 = π›Όπ‘π‘Ÿπ‘’π‘‘π‘–π‘˜π‘Žπ‘‘ 1 βˆ—π‘§1+π›Όπ‘π‘Ÿπ‘’π‘‘π‘–π‘˜π‘Žπ‘‘ 2 βˆ—π‘§2+π›Όπ‘π‘Ÿπ‘’π‘‘π‘–π‘˜π‘Žπ‘‘ 3 βˆ—π‘§3+π›Όπ‘π‘Ÿπ‘’π‘‘π‘–π‘˜π‘Žπ‘‘ 4 βˆ—π‘§4 π›Όπ‘π‘Ÿπ‘’π‘‘π‘–π‘˜π‘Žπ‘‘ 1 +π›Όπ‘π‘Ÿπ‘’π‘‘π‘–π‘˜π‘Žπ‘‘ 2 +π›Όπ‘π‘Ÿπ‘’π‘‘π‘–π‘˜π‘Žπ‘‘ 3 +π›Όπ‘π‘Ÿπ‘’π‘‘π‘–π‘˜π‘Žπ‘‘ 4 𝑧 = 79,4 1.57142857 = 50,5272728 The result of the Tsukamoto fuzzy calculation is 50.5272728, rounded up to 51 with additional estimation information. Next, an accuracy test using the RMSE is carried out. The following is a table of the results of the RMSE test on the results of accuracy of the results of the calculation of the office stationery stock estimation system using the Root mean squared error method. Table 3 Root Mean Squared Error No Nama Alternatif Manual Sistem Error Square of Error n i yi' yi yi'-yi ( yi' -yi)^2 1 Amplop Coklat F4 50,9615 42,45 8,5 72,4 2 Bantex Besar 30 30 0,0 0,0 3 Bantex Kecil 30 30 0,0 0,0 4 Binder Clip No. 200 47,7344 44,655 3,1 9,5 5 Binder Clip No. 155 57,7556 70 -12,2 149,9 6 Binder Clip No. 105 39,0909 33,333 5,8 33,2 7 Binder Clip No. 107 47,6923 46,667 1,0 1,1 8 Binder Clip No. 220 49,8983 46,833 3,1 9,4 9 Double Tape 46,2727 47 -0,7 0,5 10 Ballpoint Biru 41,1818 39,333 1,8 3,4 11 Ballpoint Hitam 45,9706 43,515 2,5 6,0 12 Ballpoint Merah 33,3333 30 3,3 11,1 13 Box File (Bindex/Gema) 16 10 6,0 36,0 14 Buku Besar 35,6889 36,8 -1,1 1,2 15 Buku Kecil 37,451 35,385 2,1 4,3 16 Buku Pencatatan Ekspedisi 54,5455 70 -15,5 238,8 17 Buku Pencatatan Km 53,2727 58 -4,7 22,3 18 Business File 47,5455 46 1,5 2,4 19 Cutter Kecil 10 10 0,0 0,0 20 Dudukan Lakban 28 28 0,0 0,0 21 Isi Cutter Kecil 15 10 5,0 25,0 22 Isi Cutter Besar 30 30 0,0 0,0 23 Isi Straples Kecil 78 70 8,0 64,0 24 Isi Straples Besar 40,0862 35,625 4,5 19,9 25 Isolatip 12Mm X 72 18,9744 10 9,0 80,5 26 Kertas 3 Play Full 25,1136 10 15,1 228,4 27 Kertas 3 Play Prs 33,1 30,4 2,7 7,3 28 Kertas 4 Play 48,8871 47,074 1,8 3,3 29 Kertas A4 49,8983 46,833 3,1 9,4 30 Kertas Karbon 38 36 2,0 4,0 Total 1043,462465 n 30 RMSE 5,897633607 Dibulatkan 5,9 The table presents a process for calculating the Root mean squared error (RMSE) where a comparison is made where manual calculations with system calculations are tested so that the total RMSE obtained in the calculation of the office stationery stock estimation system is 5.897633607 rounded up to 5.9. JURNAL RISET INFORMATIKA Vol. 5, No. 1 December 2022 P-ISSN: 2656-1743 |E-ISSN: 2656-1735 DOI: https://doi.org/10.34288/jri.v5i1.471 Accredited rank 3 (SINTA 3), excerpts from the decision of the Minister of RISTEK-BRIN No. 200/M/KPT/2020 117 RESULTS AND DISCUSSION Tsukamoto fuzzy calculations produce a recommendation value from the process that starts with strict numbers as input. Fuzzy numbers are formed through fuzzification, and then fuzzy sets are grouped through an inference process. From the inference results, a defuzzification process is to confirm numbers as output. Then the RMSE accuracy test results get a value of 5.9. RMSE can be negatively oriented, where a lower value indicates a better value. The system development resulted in a business process implemented with the Tsukamoto fuzzy method, as seen in Figure 5. Figure 5 Business Process There is an admin actor in the implementation of office stationery stock estimation with a system that has been built, which starts with inputting stock data, and then the system performs input tasks. Then the admin inputs the value to be estimated, and the system performs the task of inputting value data, then the system performs the Tsukamoto fuzzy algorithm then all the tasks that have been carried out are recorded in the system database. In the next stage, the admin can print the system calculation results. In the system, development use UML so that Class Diagram diagrams are formed describing the system's structure in terms of defining the classes that will be made to build the system. The following is a class diagram which can be seen in Figure 6. Figure 6 Class Diagram Next, the use case diagram is obtained. Use case diagrams are used to find out what functions are contained in the system and who has the right to perform these functions. This system has login functions, alternative data management, data management criteria, data management rules, calculations, and data management passwords. The following is a use case for the office stationery stock estimation system depicted in Figure 7. Figure 7 Use Case Diagram P-ISSN: 2656-1743 | E-ISSN: 2656-1735 DOI: https://doi.org/10.34288/jri.v5i1.471 JURNAL RISET INFORMATIKA Vol. 5, No. 1 December 2022 Accredited rank 3 (SINTA 3), excerpts from the decision of the Minister of RISTEK-BRIN No. 200/M/KPT/2020 118 In building this system, it is necessary to have a database design to find out how the system processes and records data in the database, the database used is MySQL. Figure 8 Database Relationship In figure 8. Shows a relational database consisting of the following: 1. Table tb_alternatif Contains office stationery stock item data consisting of kode_alternatif, nama_alternatif, and keterangan. Next, kode_alternatif is the primary key connected to the tb_rel_alternatif table. 2. Table tb_rel_alternatif The stock value of office stationery consists of id, kode_alternatif, and kode_kriteria. Next, kode_kriteria is the primary key connected to the tb_kriteria table. 3. Table tb_kriteria Contains variable criteria for office stationery stock that will be used to form a set consisting of kode_kriteria, nama_kriteria, batas_bawah, and batas_atas. Next, kode_kriteria is the primary key connected to the tb_aturan table. 4. Table tb_aturan Contains variable rules for the fuzzy inference process consisting of id_atuan, no_aturan, kode_kriteria, operator, and kode_himpunan. Next, kode_himpunan is the primary key connected to the tb_himpunan table. 5. Table tb_himpunan Contains variable set consisting of kode_himpunan,kode_kriteria,nama_himpunan. 6. Table tb_user Contains user data variables consisting of user and pass. System Implementation The implementation process into program code uses the PHP programming language. This stage is converting system specifications into an executable system to produce a system view that makes it easier for users to access each feature. 1. Dashboard Page The page displays the main page after logging in and presents the features of the office stationery stock estimation system. The following is the dashboard display which is seen in Figure 9. Figure 9. Dashboard Page 2. Nilai Page This page functions to add valuable data that will be estimated based on alternative data, criteria data, and rule data used. The following is a display of the value page, which can be seen in Figure 10. Figure 10. Nilai Page 3. Perhitungan Page This page shows the results of estimation calculations using the Tsukamoto fuzzy Method. The following displays the calculation page, which can be seen in Figure 11. Figure 11. Perhitungan Page JURNAL RISET INFORMATIKA Vol. 5, No. 1 December 2022 P-ISSN: 2656-1743 |E-ISSN: 2656-1735 DOI: https://doi.org/10.34288/jri.v5i1.471 Accredited rank 3 (SINTA 3), excerpts from the decision of the Minister of RISTEK-BRIN No. 200/M/KPT/2020 119 CONCLUSIONS AND SUGGESTIONS Conclusion Based on the author's description regarding the implementation of the Tsukamoto fuzzy method of office stationery, a stock estimation system has been prepared. Office stationery stock estimation system performs the calculation process using the Tsukamoto fuzzy method where through the process of fuzzification stages, fuzzy inference, then defuzzification, testing the accuracy of the calculation results of the office stationery stock estimation system is carried out using the Root mean squared error (RMSE) to get a value 5,9. If the RMSE value is smaller, the predicted value is close to the observed value, and the RMSE can range from 0 to ∞. 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