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All Journal Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) JMM (Jurnal Masyarakat Mandiri) JURNAL PENDIDIKAN TAMBUSAI EDUMATIC: Jurnal Pendidikan Informatika JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Bulletin of Computer Science Research Brilliance: Research of Artificial Intelligence Pelita Teknologi : Jurnal Ilmiah Informatika, Arsitektur dan Lingkungan Jurnal Ilmiah SIGMA: Informatics Engineering Journal of UPB Bulletin of Data Science Nautical: Jurnal Ilmiah Multidisiplin Indonesia Jurnal Pengabdian Mandiri Proceeding International Pelita Bangsa GLOBAL: Jurnal Lentera BITEP Jurnal Pelita Pengabdian Journal of Renewable Engineering
Amali, Amali
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Religion Aerospace Engineering Agriculture, Biological Sciences & Forestry Humanities Astronomy Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Mathematics Social Sciences Other

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Penerapan Algoritma Decision Tree Classifier untuk Klasifikasi Serangan Jantung Amali, Amali; Saputra, Muhamad Ariel; Bintang Satria, Yusuf Putra; Ramadhan, Zidan Lutfi
GLOBAL: Jurnal Lentera BITEP Vol. 3 No. 06 (2025): Desember 2025
Publisher : Lentera Ilmu Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59422/global.v3i06.367

Abstract

Menggunakan perangkat lunak RapidMiner untuk menganalisis dataset serangan jantung, penelitian ini akan menerapkan algoritma Decision Tree Classifier. Fokus utama penelitian adalah untuk mengklasifikasikan kasus positif dan negatif berdasarkan tingkat Troponin dan CK-MB. Model yang dikembangkan menunjukkan tingkat akurasi, presisi, dan recall yang tinggi, yang menunjukkan bahwa itu dapat digunakan sebagai alat diagnostik yang efektif dalam praktek klinis. Jika jenis kelamin digunakan sebagai simpul keputusan sekunder, kemampuan model untuk mengklasifikasikan kasus dengan tingkat CK-MB yang borderline meningkat. Analisis awal terhadap kadar Troponin pasien menunjukkan bahwa Troponin, CK-MB, jenis kelamin, dan usia memengaruhi risiko terkena AMI dengan Troponin positif. Evaluasi model menunjukkan akurasi sebesar 99,24%, dengan precision weighted mean recall sebesar 99,14%, dan accuracy sebesar 99,26%. Dengan menggunakan penanda biologis yang tersedia, decision tree yang dibuat dapat membantu menilai risiko serangan jantung. Penelitian ini menemukan bahwa model decision tree yang dibuat dengan RapidMiner dapat menjadi alat yang efektif untuk mengklasifikasikan kasus serangan jantung berdasarkan tingkat Troponin dan CK-MB. Tingkat akurasi dan presisi yang tinggi membuat model ini menjadi alat diagnostik yang berharga. Penelitian mendatang harus mempertimbangkan penggunaan dataset yang lebih besar dan integrasi fitur tambahan untuk meningkatkan generalisasi dan akurasi model.
Demand-Based Product Classification Using K-Means with Intermittency Metrics Anggraini, Ariska Nur; Amali, Amali; Anwar, Muhammad Syaibani
Jurnal Pendidikan Informatika (EDUMATIC) Vol 10 No 1 (2026): Edumatic: Jurnal Pendidikan Informatika
Publisher : Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29408/edumatic.v10i1.34435

Abstract

Inventory management at multi-SKU distribution companies becomes complex when most products have unstable and intermittent demand patterns. At PT JJA, procurement is still reactive without the use of historical patterns, while the previous approach generally relied on aggregate indicators such as average sales so that it has not been able to comprehensively capture temporal dynamics. This study aims to group products based on temporal demand patterns using K-Means Clustering in 11,988 transactions for the 2020–2025 period which are processed into 261 products through monthly aggregation, with features of average sales, coefficient of variation (CV), zero_month_ratio, Average Demand Interval (ADI), and trends. The results showed four optimal clusters (k = 4) with a Silhouette Score of 0.62 and an unbalanced distribution, where one cluster dominated 240 products. The values of zero_month_ratio (>0.80), ADI up to >12 months, and CV up to >3.5 show intermittent demand patterns and long-tail structures. The study confirms that the integration of temporal features (ADI, zero_month_ratio, CV, and trend) transforms the representation of demand from static aggregates to dynamic structures, while linking segmentation results with more adaptive procurement strategies to reduce the risk of overstock and understock.
Machine Learning-Based Automation in Production Processes: Enhancing Efficiency and System Accuracy in Industry Amali, Amali; Tasya, Amalia
Journal of Renewable Engineering Vol. 3 No. 2 (2026): JORE - April
Publisher : Pt. Anagata Sembagi Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62872/eqp79y32

Abstract

The integration of Machine Learning (ML) in production automation has become a key driver in transforming industrial systems into smart and adaptive manufacturing environments. This study aims to analyze the role of ML in improving efficiency and accuracy within production processes. The research employs a qualitative approach with a descriptive-analytical design, using library research and document analysis of reputable scientific sources. Data were analyzed through an interactive model consisting of data reduction, data display, and conclusion drawing. The findings reveal that ML significantly enhances operational efficiency through predictive maintenance, optimized scheduling, and real-time decision-making, while also improving accuracy in quality control through advanced algorithms such as deep learning, Support Vector Machines, and Artificial Neural Networks. Furthermore, ML enables process optimization by analyzing complex production variables and identifying optimal parameters. However, challenges such as data quality, system integration, and model interpretability remain critical barriers. The study concludes that a holistic integration of ML, supported by advanced technologies such as IIoT and Digital Twin, is essential for achieving higher efficiency, improved accuracy, and sustainable competitiveness in modern industrial systems.
Analisis Komparatif Decision Tree C4.5 dan Neural Network pada Prediksi Kanker Payudara Amali, Amali; Widodo, Edy
Bulletin of Computer Science Research Vol. 4 No. 5 (2024): August 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v4i5.295

Abstract

Breast cancer is one of the diseases with the highest incidence and mortality rates among women, requiring methods that can support fast and accurate detection. This study aims to compare the performance of the Decision Tree C4.5 and Neural Network algorithms in breast cancer classification using the Breast Cancer Wisconsin dataset obtained from the UCI Machine Learning Repository. The research method adopts the CRISP-DM approach, which includes data collection, preprocessing, model development, testing, and evaluation stages. The preprocessing stage was carried out through data cleaning, data transformation, and data reduction to improve dataset quality before the modeling process. The testing process used split validation and evaluation based on accuracy, precision, recall, and Area Under Curve (AUC) metrics. The results indicate that the Neural Network algorithm achieved better performance than Decision Tree C4.5. Neural Network obtained an accuracy of 96.17%, precision of 95.80%, recall of 96.50%, and an AUC value of 0.989, which is categorized as excellent classification. Meanwhile, Decision Tree C4.5 achieved an accuracy of 93.50% and an AUC value of 0.945, categorized as very good classification. ROC Curve analysis demonstrates that Neural Network is more effective in distinguishing benign and malignant classes. Therefore, Neural Network is recommended as the best model to support early breast cancer detection based on machine learning, while Decision Tree C4.5 remains relevant for conditions requiring simpler and more interpretable models.
Optimalisasi Strategi Penjualan Sparepart Menggunakan Association Rule Berbasis Algoritma Apriori Amali, Amali; Widodo, Edy
Bulletin of Data Science Vol 5 No 2 (2026): February 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletinds.v5i2.9903

Abstract

The development of information technology encourages companies to utilize sales transaction data as a strategic source of information in business decision-making. However, the increasing amount of transaction data is often not optimally utilized to identify consumer purchasing patterns. This study aims to analyze consumer purchasing patterns in spare parts sales transactions using association rules based on the Apriori algorithm to support the optimization of sales strategies and inventory management. The research method used is a quantitative approach consisting of data collection, data preprocessing, transaction data transformation, frequent itemset generation, and association rule formation. The data used in this study consisted of 350 spare parts sales transactions processed using the Apriori algorithm with a minimum support value of 20% and a minimum confidence value of 70%. The results showed that the products Front Bumper and Brake Pads had the strongest association relationship with a confidence value of 76% and support value of 23%. In addition, the relationship between Radiator and Side Mirror products showed a confidence value of 71%. The study proves that the Apriori algorithm is effective in identifying relationships between products and can assist companies in determining promotional strategies, inventory management, and data-driven business decision-making to improve spare parts sales
Co-Authors Al Ayubi, Muhammad Din Andri Firmansyah Andriani Andriani Andriano, Choky Angela, Devani Nadia Vita Kusuma Anggraini, Ariska Nur Anshory, Ahmad Shofwan Antika Zahrotul Kamalia Anwar, Muhammad Syaibani ARIF SUSILO Aswan Supriyadi Sunge Badruzzaman, Aceng Bintang Satria, Yusuf Putra Budiarto, Eko Budiman, Banyu Sugara Butsianto, Sufajar DHANI ARIATMANTO Edy Widodo Edy Widodo Fahmi, Alif Gunawan, Achmad Rezky Hamidah, Jilan Herfiansyah, Raihan Hidayah, Aldy Imawan, Alifia Putri Ismamudi Ismamudi Ismamudi, Ismamudi Kurniasih, Nabila Kurniawan, Jidan Restu Listyanto, Rudy Effendy Maulana, Donny Muhammad Rifa'i, Anggi Muhtajuddin Danny Nawangsih, Ismasari Noor, Viyan Qomarudin Nugroho, Agung Nurhidayanti, Nisa Permana, Yudi Pikriyansyah, Ridho Pratama, Alvian Saputra Pratama, Fauzhan Qhof Priyo, Basuki Edi Putra, Maha Ramadan, Fadli Ramadhan, Arya Syah Ramadhan, Zidan Lutfi Rifa'i, Anggi Muhammad Saepul, Muhammad Saniyah, Luzzatus Saputra, Muhamad Ariel Satpatmantyo, Kurbandi Septianto, Riski Suryadi Suryadi Tasya, Amalia Taufiq Rachman, Taufiq Tedi, Nanang Turmudi Zy, Ahmad Tyas Ismi Trialfhianty Wangsadanureja, Miftah Wibowo, Mohamad Hegar Sukmana Wiyanto -