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All Journal Jurnal Ilmiah Informatika Komputer Teknika Bulletin of Electrical Engineering and Informatics Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Jurnal Teknologi Informasi dan Ilmu Komputer Jurnal Informatika dan Teknik Elektro Terapan CESS (Journal of Computer Engineering, System and Science) Jurnal CoreIT JURNAL KAJIAN TEKNIK ELEKTRO JTAM (Jurnal Teori dan Aplikasi Matematika) METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi INTECOMS: Journal of Information Technology and Computer Science KACANEGARA Jurnal Pengabdian pada Masyarakat Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI) IJID (International Journal on Informatics for Development) JURIKOM (Jurnal Riset Komputer) Jurnal Tekno Kompak TEKNOKOM : Jurnal Teknologi dan Rekayasa Sistem Komputer Jurnal Informatika dan Rekayasa Perangkat Lunak Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi) Indonesian Journal of Electrical Engineering and Computer Science Bubungan Tinggi: Jurnal Pengabdian Masyarakat Jurnal Manajemen Informatika Jayakarta International Journal Software Engineering and Computer Science (IJSECS) Berdikari : Jurnal Pengabdian kepada Masyarakat Malcom: Indonesian Journal of Machine Learning and Computer Science Technology and Informatics Insight Journal KAMI MENGABDI Journal of Data Science Theory and Application Journal of Digital Business and Management Prosiding Seminar Nasional Rekayasa dan Teknologi (TAU SNAR- TEK) Jurnal Indonesia : Manajemen Informatika dan Komunikasi Edusight International Journal of Multidisciplinary Studies (EIJOMS) International Journal of Law Social Sciences and Management Computer Journal
Tundo, Tundo
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Fuzzy Inference System Tsukamoto–Decision Tree C 4.5 in Predicting the Amount of Roof Tile Production in Kebumen Tundo, Tundo; Mahardika, Fajar
JTAM (Jurnal Teori dan Aplikasi Matematika) Vol 7, No 2 (2023): April
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jtam.v7i2.13034

Abstract

Tile is a product that is in great demand by many people. This has become a trigger for producers to improve their management. The company's tile production management is still experiencing problems, namely frequent miscalculations in determining the agreement that must be issued in making tile production from customer requests. One of the efforts made is to predict the production that can be done to get the optimal amount obtained, to get a big profit. In this study, to obtain a prediction of the amount of tile production, computerized calculations were carried out using the Tsukamoto fuzzy logic method. This method uses the concept of rules from the C 4.5 decision tree in the building to make it easier to determine the rules that are built without having to consult an expert because C 4.5 will study existing datasets to serve as a reference in forming these rules according to conditions that often occur. The modeling results produce relevant rules after being compared with the actual results. The results of the comparison of predictions with actual production have an error percentage of 29.34%, with a truth of 70.66% (based on the calculation of the Average Forecasting Error Rate (AFER)). Therefore when implemented in the Tsukamoto Fuzzy Inference System it can produce predictions of tile production that are quite optimum. It is said to be quite optimum because all customer requests are met, either generated by the production prediction itself or the prediction results are added up with inventory data, and all predictions are close to actual production.
Application of the Apriori Algorithm in Transaction Data in Rumah Makan Murah Marthy, Nicola; Tundo, Tundo; Nabilah, Laila; Maharani, Delia
Edusight International Journal of Multidisciplinary Studies Vol. 1 No. 2 (2024): Edusight International Journal of Multidisciplinary Studies
Publisher : Yayasan Meira Visi Persada

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.69726/eijoms.v1i2.36

Abstract

This research aims to apply the Apriori algorithm in transaction data analysis at a budget-friendly restaurant to identify purchasing patterns and relationships between frequently bought items. By leveraging historical transaction data, the Apriori algorithm can discover significant associations among various menu items, which can then be used to develop more effective marketing strategies, optimize product placement, and boost sales. The research process includes the collection and preprocessing of transaction data, application of the Apriori algorithm for association rule extraction, and analysis and interpretation of the results. The findings from this study are expected to provide valuable insights for budget-friendly restaurant managers to develop more efficient, data-driven business strategies.
Application of the K-Nearest Neighbor Method in Determining Laptop for Classes Qolbi, Rofika; Tundo, Tundo; Putri Wibowo, Salsabila; Akbar, Yuma
International Journal of Law Social Sciences and Management Vol. 1 No. 3 (2024): International Journal of Law Social Sciences and Management
Publisher : Yayasan Meira Visi Persada

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.69726/ijlssm.v1i3.31

Abstract

Laptops are one of the basic needs in today's modern life. Laptops are used in a wide variety of activities such as work, study, and entertainment. This research aims to be able to predict the class of laptops in the Ilda Computer store. In this process, the K-Nearest Neighbor Algorithm (KNN) method will be applied. There are 2 types of data that will be used in this study, namely training data totaling 80 data and test data as many as 6 data. In the data, there are 7 criteria that will be used, namely Price, Screen Size, Resolution, OS, RAM, Processor Type, and Laptop Class. In this study, it was obtained that the application of the KNN Algorithm can help in determining the prediction of the Laptop Class. And also the application of the KNN algorithm with K=3 obtained the best performance results with an accuracy value of 50%, a presicion of 50%, and a recall of 66%. Meanwhile, with K=4, the best performance results were obtained with an accuracy value of 50%, presicion of 66%, and recall of 50%. Finally, the K=5 obtained the best performance with an accuracy value of 66%, a presicion of 33%, and a recall of 100%.
Subjectivity Tracking System for Poor Scholarship Recipients at Elementary School Using the MOORA Method Tundo, Tundo
JTAM (Jurnal Teori dan Aplikasi Matematika) Vol 6, No 3 (2022): July
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jtam.v6i3.8373

Abstract

This research was conducted because of complaints from several parents at Elementary School regarding recipients of the Poor Student Assistance (PSA) who were still less objective. Elementary School XY regularly conduct screening activities every year to select prospective PSA recipients. This selection is made so that the recipients of this assistance are students entitled to it. Some students should be accepted as a selection committee but do not mistake of choosing some students who have kinship or subjective matters. Therefore, this study aims to explore and create applications that apply the Multi Objective Optimization to the basic  Ratio Analysis (MOORA) method, which is a method for determining students based on predetermined criteria. The criteria used are the value of report cards, student achievement, student activity, parental income, parental dependents, and home conditions. After conducting a search and implementation using the MOORA method in determining PSA recipients, it was found that there were some non-objective results where the student's criteria and final results were lower than some other students. However the Elementary School provided a recommendation to get PSA. If this happens again, then the importance of this system is to help objective selection. The accuracy results explained that 14.39% of PSA recipients were subjective. It was concluded that this research helps an objective decision and facilitates the decision maker in determining the best 3 recipients from each class at Elementary School XY.
Penerapan Metode Double Moving Average Untuk Memprediksi Penjualan Tiket Bus Sinar Jaya Po Tambun Tundo, Tundo; Nugroho, Agung Yuliyanto; Saidah, Andi
Jurnal Informatika dan Rekayasa Perangkat Lunak Vol. 7 No. 2 (2025): September
Publisher : Universitas Wahid Hasyim

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

The Sinar Jaya Autobus Company (PO) is one of the buses engaged in the tourism business that sells and provides community needs such as bus tickets. This PO requires forecasting in data processing to produce accurate reports. The reason for this is because PO Bus Sinar Jaya in determining the demand for bus tickets cannot predict availability. Based on these reasons, the design of this system uses the Double Moving Average (DMA) forecasting method for the forecasting process in determining the amount and type of availability that will be sold for the following month. By using this calculation method it is hoped that the owner of PO Sinar Jaya will further optimize the things that can be detrimental to this PO in operating. If sales increase each month, using the DMA method, sales predictions for the next three months can be determined, the higher the number of ticket requests on the PO Sinar Jaya Bus, so that the forecasting results can help the PO to avoid running out of tickets according to consumer demand. Based on the research that has been carried out, it can be concluded that the Sinar Jaya PO Tambun bus ticket sales forecast using the Double Moving Average (DMA) method obtained the smallest MAPE value calculation results in order 2, namely 0.004599299 and the smallest MAPE value in order 3, namely 0.000614191. Comparison of the results of MAPE value calculations to determine the accuracy of forecasting results carried out with order 2 and order 3, it is proven that order 3 is more accurate for determining the error percentage results in this study.
Evaluasi Kepuasan Mahasiswa terhadap Fasilitas Kampus Menggunakan Metode Simple Additive Weighting (SAW) Nugroho, Agung Yuliyanto; Tundo, Tundo; Saidah, Andi
Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi) Vol 10 No 2 (2026): APRIL 2026
Publisher : Lembaga Otonom Lembaga Informasi dan Riset Indonesia (KITA INFO dan RISET)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/jtik.v10i2.5324

Abstract

Evaluation of student satisfaction with campus facilities is one important aspect in improving the quality of college services and infrastructure. However, this satisfaction evaluation process has a gap between student expectations of the facilities provided by the campus. This research aims to design a Decision Support System (DSS) that can help the campus in assessing student satisfaction levels using the Simple Additive Weighting (SAW) method. This method was chosen because of its ability to conduct an assessment based on criteria that have certain weights so as to produce the best alternative ranking. The criteria used in this study include cleanliness, maintenance, comfort, completeness, condition, service, and level of satisfaction. Data was obtained through questionnaires distributed to students in various study programs, focusing on various aspects of campus facilities such as classrooms, laboratories, libraries, hall areas, and podcast studios. It was then processed using SAW steps, including matrix normalization and final score calculation. The result showed that the final results of the evaluation of student satisfaction with campus facilitiesf from 100 respodents obtained a fairly high score of 44 which was said to be quite good. This SAW method is able to provide a clear ranking of the level of student satisfaction, as well as identify areas that require improvement or enchancement at the college.
PENGARUH E-WOM DALAM MEMEDIASI HUBUNGAN ANTARA DIGITAL MARKETING ACTIVITIES DAN INTENTION TO BUY DI TOKOPEDIA Heri Mahyuzar; Tundo, Tundo
Journal of Digital Business and Management Vol. 1 No. 2 (2022): Journal of Digital Business and Management
Publisher : LP3M Universitas Putra Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32639/jdbm.v1i2.182

Abstract

Penelitian ini bertujuan untuk menganalisis pengaruh electronic word of mouth (E-WOM) dalam memediasi hubungan antara digital marketing activities dan intention to buy di Tokopedia. Populasi pada penelitian ini adalah semua pengguna Tokopedia di Indonesia. Adapun sampel penelitian yang digunakan pada penelitian ini sebanyak 100 responden yang dipilih berdasarkan convinience sampling. Teknik pengumpulan data dalam penelitian ini menggunakan metode survey dengan cara mengirimkan kuesioner kepada responden. Alat analisis yang digunakan adalah SEM AMOS. Berdasarkan analisis yang telah dilakukan diperoleh bahwa content marketing berpengaruh positif signifikan terhadap electronic word of mouth (E-WOM). Electronic Promotion berpengaruh positif signifikan terhadap electronic word of mouth (E-WOM). Electronic word of mouth (E-WOM) berpengaruh positif signifikan terhadap intention to buy. Sedangkan electronic word of mouth (E-WOM) dapat memediasi hubungan antara digital marketing dan intention to buy.
Implementasi Clustering Prestasi Siswa Mata Pelajaran IPS Melalui Visual Studio Code Raihanah, Syifa; Tundo, Tundo; Wahyudi, Tri; Sugiyono, Sugiyono
TEKNOKOM Vol. 8 No. 1 (2025): TEKNOKOM
Publisher : Department of Computer Engineering, Universitas Wiralodra

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31943/teknokom.v8i1.221

Abstract

Penelitian ini mengoptimalkan data prestasi siswa dalam mata pelajaran IPS di SMP Ksatrya Jakarta melalui metode K-means clustering. Menggunakan metodologi CRISP-DM, data prestasi seperti nilai harian, sumatif tengah semester, sumatif akhir tahun, dan rapor dikumpulkan dan diolah. Hasil analisis menunjukkan tiga kategori siswa: prestasi sangat baik, baik, dan cukup. Nilai silhouette score menunjukkan cluster 2 memiliki kecocokan tertinggi (0.2745), sedangkan cluster 1 terendah (0.0585), mengindikasikan cluster 2 lebih terdefinisi. Pengelompokan ini diperoleh melalui aplikasi desktop yang dikembangkan dengan Visual studio code dan Tkinter. Implementasi clustering ini mendukung pembelajaran lebih efektif dan personal dengan materi menantang untuk siswa berprestasi sangat baik, sesuai tingkat pemahaman untuk siswa berprestasi baik, dan berbasis pengulangan untuk siswa berprestasi cukup, memberikan wawasan mengenai pola prestasi siswa dan mendukung keputusan pembelajaran yang lebih terarah.
SHARING KNOWLEDGE: DECISION SUPPORT SYSTEM DALAM MEMBUAT BERBAGAI KEPUTUSAN YANG RASIONAL Tundo, Tundo; Saidah, Andi
KAMI MENGABDI Vol 5, No 2 (2025): KAMI MENGABDI
Publisher : Universitas 17 Agustus 1945 Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52447/km.v5i2.8640

Abstract

Kegiatan pengabdian kepada masyarakat ini bertujuan untuk meningkatkan pemahaman dan keterampilan siswa-siswi SMA Yappenda Jakarta Utara dalam menggunakan Decision Support System (DSS) sebagai alat bantu dalam pengambilan keputusan yang rasional dan terstruktur. Pengambilan keputusan yang tepat sangat penting dalam kehidupan akademik maupun sehari-hari, terutama di era digital saat ini yang sarat dengan informasi dan pilihan. Melalui kegiatan sharing knowledge ini, para peserta diperkenalkan pada konsep dasar DSS, jenis-jenis sistem pendukung keputusan, serta implementasinya dalam situasi nyata yang relevan dengan kehidupan pelajar. Metode yang digunakan dalam kegiatan ini meliputi ceramah interaktif, studi kasus, serta simulasi pengambilan keputusan berbasis teknologi. Hasil dari kegiatan ini menunjukkan peningkatan pemahaman peserta terhadap pentingnya pendekatan sistematis dalam membuat keputusan serta kemampuan awal dalam mengidentifikasi masalah, menganalisis alternatif, dan memilih solusi terbaik menggunakan prinsip DSS. Kegiatan ini diharapkan dapat menjadi bekal awal bagi siswa dalam menghadapi tantangan pengambilan keputusan di masa depan secara lebih rasional dan terukur.
Automatic Detection of Skin Diseases Using Convolutional Neural Network Algorithms Tundo; Fadillah Abi Prayogo; Sugiyono
International Journal Software Engineering and Computer Science (IJSECS) Vol. 4 No. 3 (2024): DECEMBER 2024
Publisher : Lembaga Komunitas Informasi Teknologi Aceh (KITA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/ijsecs.v4i3.3021

Abstract

Skin diseases are a major health concern in Indone sia and they can seriously impact a patient’s quality of life. The problem is aggravated by humid tropical climate, limited access to healthcare facilities, and a lack of trained dermatology personnel. The cases in Indonesia are many, and the diagnosis and treatment of skin diseases are delayed, which makes the patient's condition worse. Based on data from the Ministry of Health (Kemenkes), the prevalence of skin disease in Indonesia is 0.62 cases per 10,000 population with the highest prevalence in Eastern Indonesia. Developing a Skin Disease Detection System Based on Convolutional Neural Network (CNN) algorithms. However, CNN algorithms are widely used in image recognition and classification, and can act as an automatic diagnostic system. This system has been developed to aid in diagnosis and improve patient access to dermatological care, especially for remote communities. Users can reach out for services at any time and any location, a practical solution for treating skin health problems. This study's results are anticipated to lower the diagnostic delays and improve the treatment outcomes while offering quick access to reliable dermatological service. This is a great effort on global level for any skin disease supporting to improve life of human lives from skin health issues.
Co-Authors Abdus Salam, Abdus Agung Yuliyanto Nugroho Ahmad Satria Rizqi Maula Akbar, Rasyan Akbar, Riolandi Akbar, Yuma Alief Prima Gani Amelia, Ika Anisah Nurul Azhar Arinal, Veri Arvianto, Ramdani Aryanti, Putri Gea Atsilah Daini Putri Aula, Raisah Fajri Aulia Nur Septiani Azhar, Anisah Nurul Betty Yel, Mesra Betty Yel, Mesra Bobby Arvian James Dadang Iskandar Mulyana` Dalail Dalail Dalail, Dalail Devia, Elmi Dewantara, Rizki Dewanti, Elsa Mayorita Dharmawan, Tio Dita Tri Yuliantoro Doni Kurniawan Doni Kurniawan Eldina, Ratih Enny Itje Sela Fadillah Abi Prayogo Fakhrurrofi Fakhrurrofi Fakhrurrofi, Fakhrurrofi Faldo Satria Faridatun Nisa Farras Abiyyu Handoko Fauzan Ibnu Sarky Galih Satria Yacob Gatra, Rahmadhan Hadi Gunawan, Hadi Haryati Heri Mahyuzar Heri Mahyuzar Humam Mu'asyir Husain Rahmani James, Bobby James, Bobby Arvian Januarsyah, Firly Joko Sutopo Junaidi Junaidi Kasiono, Roy Kastum Kastum Kastum Kastum, Kastum Kevin Arya Josaphat Sitompul Khafid Nurohman Khana, Rajes Kiki Setiawan Laily Nurmayanti Laras Sitoayu Lutfi Nugrahaini M. A. Burhanuddin Maharani, Delia Maharani, Shinta Aulia Mahardika, Fajar Mahyuzar, Heri Marcia Rizky Hamdala Marliani, Tiara Marthy, Nicola Mohd Khanapi Abd Ghani Mubarak, Zulfikar Yusya Muhammad Derry Oktaviandi Muhammad Nurdin Muhammad Nurdin Muhammad Raffiudin Muhammad Syazidan Nabilah, Laila Nandang Sutisna Nandang Sutisna Nisa, Faridatun Nizar, Amin Nugraha, Pramudya Nugrahaini, Lutfi Nugroho, Agung Yuliyanto Nugroho, Wisnu Dwi Nuradi, Fahmi Nurohman, Khafid Opi Irawansah, Opi Paidi, Imam Pramudya Nugraha Prayogo, Fadillah Abi Priyanto, Imansyah Purwasih, Intan Putri Wibowo, Salsabila Qolbi, Rofika Rachmat Hidayat Insani Rachmat Hidayat Insani Rachmawati, Dea Noer Raden Dewa Saktia Purnama Raffiudin, Muhammad Raihanah, Syifa Raisah Fajri Aula Ramadhan, Abhirama Huga Ramadhani, Devika Azahra Rasiban Rasiban Ridho Akbar Rindy Julianda Riolandi Akbar Rizki Maulana, Rizki Rohmat Wijaya Romadan, Diva Putra Rona Guines Purnasiwi Saidah, Andi Saifullah, Shoffan Saktia Purnama, Raden Dewa Sarimole, Frencis Matheos Setiawan, Kiki Shindy Apriani Shofwatul ‘Uyun Sodik Sopan Adrianto SOPAN ADRIANTO SRI LESTARI Sugeng Sugiono Sugiono Sugiyono Sugiyono Sugiyono Sugiyono Suropati, Untung Sutisna, Nandang Syani, Muhammad Syifa Raihanah Tampubolon, Parlindungan Tasti, Andi Thalita Tiara Ratu Alifia Tresia, Eflin Tri Wahyudi Tri Wahyudi Tundo Tundo Untung Suropati Untung Suropati Wafiqi, Achmad Ulul Azmi Wagiman, Wagiman Waloeya, Farhan Adriansyah Wijonarko, Panji Wisnu Dwi Nugroho Yacob, Galih Satria Yudisman Ferdian Bili