https://www.ejournal.techcart-press.com/index.php/chain/issue/feed CHAIN: Journal of Computer Technology, Computer Engineering, and Informatics 2026-07-01T00:00:00+07:00 Desi Puspitasari, M.Kom. techcartpress@gmail.com Open Journal Systems <p align="justify"><strong>CHAIN: Journal of Computer Technology, Computer Engineering and Informatics</strong> is a peer-review journal focusing on Computer Technology, Computer Engineering and Informatics. CHAIN invites academics and researchers who do original research in computer technology, computer engineering and informatics. CHAIN: Journal of Computer Technology, Computer Engineering and Informatics are published by <strong>Tech Cart Press</strong> in<strong> January, April, July, and October every year</strong>. CHAIN: Journal of Computer Technology, Computer Engineering and Informatics accept articles in Bahasa Indonesia and English.</p> <p align="justify">CHAIN: Journal of Computer Technology, Computer Engineering and Informatics has ISSN <a href="https://issn.brin.go.id/terbit/detail/20221120162079549" target="_blank" rel="noopener">2964-2485 (Online)</a> in accordance with the letter of Statement Number 29642485/II.7.4/SK.ISSN/12/2022, and ISSN <a href="https://issn.brin.go.id/terbit/detail/20221120232018935" target="_blank" rel="noopener">2964-2450 (Print)</a> in accordance with the letter of Statement Number 29642450/II.7.4/SK.ISSN/12/2022.</p> <p align="justify">We are proud to announce that our journal has successfully achieved accreditation from the <strong>Kementerian Pendidikan, Kebudayaan, Riset, dan Teknologi with Number: 156/C/C3/KPT/2026</strong> with a <strong>SINTA 5</strong>. This achievement is the result of the dedication and hard work of the editorial team, reviewers, and writers who have contributed to maintaining the quality of the published articles. With this accreditation, we are committed to continuing to improve the quality and relevance of published research, as well as expanding the scientific impact on the national and international scope. Thank you to all parties who have supported the development of our journal. We hope that this journal will continue to be a forum for the publication of high-quality scientific works in the future.</p> <p align="justify"><img src="/public/site/images/adminojstcp/CHAIN.png" width="100%"></p> https://www.ejournal.techcart-press.com/index.php/chain/article/view/255 Penerapan Fuzzy Multiple Attribute Decision Making pada Pemilihan Lokasi Perumahan Menggunakan Metode TOPSIS 2026-05-14T17:24:14+07:00 Donaya Pasha donayapasha@itsnulampung.ac.id Ari Sulistiyawati ari_sulistiyawati@teknokrat.ac.id <p>Pemilihan lokasi perumahan merupakan salah satu proses pengambilan keputusan yang cukup kompleks karena calon pembeli sering mengalami kesulitan dalam menentukan perumahan terbaik berdasarkan berbagai kriteria yang harus dipertimbangkan secara bersamaan, seperti akses lokasi, sarana dan prasarana, kondisi lingkungan, harga, dan kondisi jalan. Perbedaan tingkat kepentingan setiap kriteria serta adanya penilaian yang bersifat subjektif menyebabkan proses pemilihan menjadi kurang efektif apabila dilakukan secara manual. Oleh karena itu, penelitian ini bertujuan untuk menerapkan metode Fuzzy Multiple Attribute Decision Making (FMADM) menggunakan metode TOPSIS dalam memberikan rekomendasi pemilihan lokasi perumahan terbaik. Metode TOPSIS menggunakan matriks keputusan yang menggambarkan hubungan antara kriteria dan alternatif, di mana matriks tersebut berisi nilai-nilai kriteria yang menunjukkan kinerja relatif setiap alternatif terhadap masing-masing kriteria. Berdasarkan hasil perangkingan, alternatif yang memperoleh peringkat pertama adalah Arum Lestari dengan nilai sebesar 0,561737951, peringkat kedua adalah Springhill dan Amaya Residence dengan nilai sebesar 0,522774425, peringkat ketiga adalah Citra Garden dengan nilai sebesar 0,5, dan peringkat keempat adalah Bumi Asri Residence dengan nilai sebesar 0,477225576.</p> 2026-07-01T00:00:00+07:00 Copyright (c) 2026 Donaya Pasha, Ari Sulistiyawati https://www.ejournal.techcart-press.com/index.php/chain/article/view/264 Objective Approach in Supplier Selection: Integration of RECA Weighting and Combinative Distance-based Assessment Method 2026-05-20T11:07:18+07:00 Setiawansyah Setiawansyah setiawansyah@teknokrat.ac.id Iryanto Chandra iryanto.chandra@uin-suka.ac.id <p>Supplier selection is a strategic decision that directly affects operational efficiency and supply chain performance. This study aims to propose a multi-criteria decision-making approach to evaluate and rank suppliers objectively based on multiple performance indicators. The evaluation is conducted using five main criteria, namely price, quality, delivery, responsiveness, and capacity and flexibility. A total of nine supplier alternatives were assessed, and a quantitative decision model was applied to aggregate the performance of each alternative into a final score and ranking. The results indicate that the proposed approach is capable of clearly distinguishing supplier performance, as reflected in the significant differences in final scores across alternatives. The ranking results show that PT Cipta Solusi Persada achieved the first position with a final score of 0.5171, followed by PT Karya Nusantara with a score of 0.4626 in the second position, and PT Prima Logistik Indonesia with a score of 0.3922 in the third position. These findings demonstrate that suppliers with balanced performance across all criteria tend to achieve higher rankings. The study also highlights that suppliers with lower rankings generally exhibit structural weaknesses in key criteria, suggesting the need for performance improvement or strategic reconsideration.</p> 2026-07-01T00:00:00+07:00 Copyright (c) 2026 Setiawansyah Setiawansyah, Iryanto Chandra https://www.ejournal.techcart-press.com/index.php/chain/article/view/274 Applying Analytical Hierarchy Process in a Decision Support System for Study Program Recommendation 2026-05-24T12:06:52+07:00 Aldyth Najma Rova Marthin aldythmarthin106@student.unsrat.ac.id Mahardika Inra Takaendengan mahardika@unsrat.ac.id Marline Sofiana Paendong marlinepaendong@unsrat.ac.id <p>Choosing a study program is a critical academic decision because it affects students' learning direction, skill development, and career readiness. This study designs, implements, and evaluates a web-based Decision Support System for study program recommendation using the Analytical Hierarchy Process. The model uses four criteria: interest and talent, technology-related hobby, academic score, and job prospects. The research used teacher criteria data before web implementation and student alternative data after the system was implemented. Teacher matrices were screened using the Consistency Ratio requirement, and the valid matrix produced criteria weights of 0.436 for interest and talent, 0.320 for job prospects, 0.192 for technology-related hobby, and 0.053 for academic score. The system was developed with Python and Flask, then evaluated using Black Box Testing and User Acceptance Testing. The main scenario produced Informatics Engineering as the first recommendation with a score of 0.4880 or 49 percent. Across 24 post-implementation student responses, Informatics Engineering was also the most frequent top recommendation, appearing in 10 responses, followed by Mathematics in 9 responses. However, only 16 of 96 student alternative matrices met the CR threshold, which indicates that automatic consistency validation is needed. Black Box Testing confirmed that all tested core functions worked as expected, and UAT produced an acceptance percentage of 81 percent. These results show that the proposed system can provide systematic and usable recommendation support, while consistency control remains the main technical improvement needed.</p> 2026-07-01T00:00:00+07:00 Copyright (c) 2026 Aldyth Najma Rova Marthin, Mahardika Inra Takaendengan, Marline Sofiana Paendong https://www.ejournal.techcart-press.com/index.php/chain/article/view/272 Sistem Irigasi Drip Otomatis Menggunakan Metode Extreme Programming Berbasis Internet of Things 2026-05-27T13:49:12+07:00 Stephano Caesar Wenston Ngangi stephano.ngangi@unsrat.ac.id Aditya Lapu Kalua adityalapu.kalua@unsrat.ac.id Jelly Ribka Danaly Lumingkewas jellylumingkewas@unsrat.ac.id Eric Alfonsius ericlenak@gmail.com <p>Pertumbuhan teknologi yang pesat, terutama di bidang Internet of Things (IoT), telah membuka peluang besar untuk mengatasi berbagai masalah dalam sektor pertanian, termasuk irigasi. Salah satu masalah utama yang dihadapi oleh petani adalah inefisiensi dalam penggunaan air, terutama pada sistem irigasi konvensional yang seringkali kurang akurat dalam mendistribusikan air secara tepat sesuai kebutuhan tanaman. Sistem manual juga memerlukan tenaga dan waktu yang signifikan, yang berpotensi menghambat produktivitas.Untuk mengatasi masalah ini, penelitian ini mengusulkan pengembangan sistem irigasi tetes otomatis berbasis IoT menggunakan metode Extreme Programming (XP). Solusi ini melibatkan penggunaan sensor kelembaban tanah dan mikrokontroler yang terhubung ke jaringan internet, memungkinkan pemantauan dan pengendalian irigasi secara real-time. Melalui penerapan metode XP, sistem ini dirancang secara iteratif dengan pendekatan yang fleksibel, memudahkan penyesuaian terhadap kebutuhan pengguna dan kondisi lapangan. Hasil dari pengembangan menunjukkan bahwa sistem irigasi tetes otomatis ini mampu meningkatkan efisiensi penggunaan air hingga 30% dibandingkan sistem manual. Sistem berhasil diimplementasikan dan diuji pada skala hidroponik, dengan hasil pertumbuhan tanaman yang lebih baik dan pengurangan pemborosan air. Antarmuka sistem berbasis web juga memungkinkan pengguna untuk memantau kondisi tanah dan mengatur waktu irigasi secara jarak jauh. Penerapan sistem irigasi tetes otomatis berbasis IoT dengan metode XP memberikan solusi efektif untuk mengatasi inefisiensi irigasi di sektor pertanian. Sistem ini tidak hanya memudahkan pengelolaan air, tetapi juga mendukung keberlanjutan dengan cara yang hemat energi dan sumber daya.</p> 2026-07-01T00:00:00+07:00 Copyright (c) 2026 Stephano Caesar Wenston Ngangi, Aditya Lapu Kalua, Jelly Ribka Danaly Lumingkewas, Eric Alfonsius