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All Journal Jurnal Ilmu Komputer dan Informasi Jurnal ELTIKOM : Jurnal Teknik Elektro, Teknologi Informasi dan Komputer Sinkron : Jurnal dan Penelitian Teknik Informatika Specta Journal of Technology Scientific Journal of Informatics ILTEK : Jurnal Teknologi Journal of System and Computer Engineering Jurnal Pengabdian Masyarakat Abdimas Madani Patria Artha Journal of Community Bulletin of Information Technology (BIT) Jurnal Nasional Teknik Elektro dan Teknologi Informasi Jurnal Teknologi dan Komputer ILTEK : Jurnal Teknologi Jurnal Pengabdian Sosial JIKTIF
Nursuci Putri Husain
Universitas Islam Makassar
Religion Aerospace Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Library & Information Science Mechanical Engineering Nursing Public Health Social Sciences

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Journal : Bulletin of Information Technology (BIT)

 1 
Rekomendasi Sparepart Pada Bengkel Robbi Motor Berbasis Algoritma Apriori Suharni; Putri Husain, Nursuci; Atsari Hardiman, Ashriyanto
Bulletin of Information Technology (BIT) Vol 6 No 2: Juni 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bit.v6i2.2024

Abstract

The development of transportation, especially two-wheeled motorized vehicles, drives the increasing demand for maintenance services and the availability of spare parts. However, many workshops still face challenges in managing spare parts stock, which is handled manually. This study aims to design and develop a spare parts recommendation system at Robbi Motor Workshop using the Apriori Algorithm, as well as to test the performance of the developed system. The method used is data mining with association techniques, where the Apriori Algorithm is applied to discover spare parts purchasing patterns from transaction data. The system enables users to analyze transactions based on a selected time range without the need to manually input minimum support and confidence values. The results show that the system is capable of generating relevant association rules, such as: “If consumers buy Engine Oil, then consumers will also buy Axle Oil”, with a support value of 67% and a confidence value of 86%. In addition, the system’s accuracy was tested using the lift value against two recommendation rules: (1) Engine Oil → Axle Oil with a lift value of 0.9949, and (2) Inner Tire → Axle Oil with a lift value of 1.0714. A lift value > 1 indicates that the combination of items has a stronger association than random occurrence. The system is implemented as a web-based application using the Laravel framework, equipped with features for transaction data management, Apriori analysis, analysis history, and exporting analysis results to PDF format. Testing using the blackbox method shows that the system operates according to specifications and produces accurate outputs. With this recommendation system, it is expected that the workshop can improve the efficiency of spare parts stock management.
Sistem Pakar Berbasis AI dengan Artificial Neural Networks untuk Identifikasi Hama & Penyakit Jamur Tiram Husain, Nursuci Putri; Mirnawaty Sultan, Dian
Bulletin of Information Technology (BIT) Vol 6 No 3: September 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bit.v6i3.2208

Abstract

Oyster mushroom cultivation is an agricultural sector with high economic potential, but its productivity is often disrupted by pests and diseases. Inappropriate management due to farmers' limited knowledge can cause significant losses. This study aims to develop an expert system for oyster mushroom pest and disease diagnosis based on Artificial Neural Networks (ANN), to assist in early identification of emerging disorders. The dataset consists of 150 samples covering a combination of symptoms and disease labels, collected from two different cultivation locations. There are several stages in this study, namely the preprocessing process that includes label encoding, feature normalization using Z-score, and data division in a ratio of 80% for training and 20% for testing. The ANN model was designed using a Multi-Layer Perceptron (MLP) with two hidden layers containing 10 neurons each, a ReLU activation function, an Adam solver, and a maximum iteration of 1000. The test results showed the model has an accuracy rate of 97%, with perfect precision and recall values ​​for most disease classes. This study shows that the ANN approach is able to effectively recognize oyster mushroom disease symptom patterns. This system can be an efficient and adaptive diagnostic tool, and has the potential to be further developed as a smart agricultural technology solution
Co-Authors Agus Zainal Arifin Ahmad Hanafie Ainun Nur, Rikhzan Akhsa, Amaliah Chintami Darti Akmal, Maisyal Arwinsa, Arwinsa Atsari Hardiman, Ashriyanto Baco, Syarifudddin Burhanuddin, Nur Isnayanti Chastine Fatichah Darlis Herumurti Dedi Susanto El Fazza, Fahri Faridah Faridah Fitri, Rahmadhany Futriani, Riski H. Halim, Nur Indah Sari Hamdan Arfandy Hanafie, Ahmad Hariati Kumkelo Herlinah B Herlinah B Herwinsyah Ichwan K, Muh Ichwan K, Muh. Jabar, Rahul Jainab, Zul efendi Junaedy Junaedy M Akbar Mardawiah MARIAM Martani, Ahmad Mirnawaty Sultan, Dian Mithahul Khairah Muh. Dzulkifli P Muh. Ichwan Kadir Musrawati Mutmainnah Mutmainnah Mutmainnah Mutmainnah Nurhidayah, Ulfa Nursanti Novi Arisa Nurseno Bayu Aji Nurseno Bayu Aji, Nurseno Bayu Putri Nur Rahayu Ramadhan, Fatwa Ramli, Ryan Midzar Wiradinata Rantau Jati, Tanto Dwi Ratu, Ratu Ameliah Ria Rahmi Putri Rosmiati Sajiah, Sajiah Sandre, Revas Siti Eti Tridamayanti Soasiu, Munawar Suharni Sukirman Sukirman Sukirman Suradi Suradi Suradi Suradi Syarifuddin Baco Syarifuddin Baco Tridarmayanti, Siti Eti Tukmuly, Asyuni