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All Journal JURNAL PENDIDIKAN TAMBUSAI Data Science: Journal of Computing and Applied Informatics Joined Journal (Journal of Informatics Education Journal of Artificial Intelligence and Digital Business IKHLAS: Jurnal Pengabdian Dosen dan Mahasiswa Journal of Systems, Information Technology, and Electronics Engineering Information Technology Education Journal Jurnal Teknologi Informasi Indonesia
Nur Alinuddin Kaharu
STMIK Adhi Guna
Automotive Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Law, Crime, Criminology & Criminal Justice Medicine & Pharmacology Social Sciences Other

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Penerapan Metode Hybrid Topsis-Moora untuk Sistem Pendukung Keputusan Pemilihan Supplier Kopi Terbaik pada SIN COFFEE Palu Sukardi, Sukardi; Kaharu, Nur Alinuddin
RIGGS: Journal of Artificial Intelligence and Digital Business Vol. 4 No. 4 (2026): November - January
Publisher : Prodi Bisnis Digital Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/riggs.v4i4.5397

Abstract

SIN COFFEE, sebuah usaha coffee shop yang beroperasi di kawasan Tondo, Kota Palu, menghadapi tantangan strategis dalam manajemen rantai pasok. Ketergantungan pada berbagai supplier biji kopi di Sulawesi Tengah sering kali menimbulkan kendala dalam menjaga konsistensi stok dan standar rasa. Masalah utama terletak pada kompleksitas pengambilan keputusan manual yang rentan terhadap subjektivitas, mengingat banyaknya alternatif supplier dengan keunggulan yang bervariasi pada setiap kriteria. Oleh karena itu, penelitian ini bertujuan membangun Sistem Pendukung Keputusan (SPK) berbasis web yang objektif dan terkomputerisasi.Sistem ini dikembangkan menggunakan framework Laravel dan database MySQL, menerapkan metode hybrid yang menggabungkan TOPSIS dan MOORA. Sinergi kedua metode ini dipilih untuk meningkatkan akurasi: TOPSIS berperan vital dalam melakukan normalisasi data dan pembentukan matriks terbobot Yi berdasarkan preferensi kriteria, sementara MOORA digunakan untuk tahap akhir dalam menghitung nilai optimasi Yi dan menentukan peringkat alternatif secara presisi. Kriteria evaluasi yang ditetapkan meliputi harga, kualitas biji kopi, kecepatan waktu pengiriman, kapasitas suplai, serta kepemilikan sertifikasi mutu.Hasil pengujian sistem menunjukkan kinerja yang signifikan dalam mengotomatisasi perhitungan yang rumit. Berdasarkan analisis data, Kulawi terpilih sebagai supplier terbaik dengan perolehan nilai Yi tertinggi sebesar 0,0638. Keunggulan Kulawi didorong oleh performa yang paling stabil dan konsisten di seluruh kriteria, khususnya pada aspek mutu tinggi dan keandalan pengiriman. Implementasi sistem ini terbukti mampu meningkatkan efisiensi operasional dan objektivitas manajemen SIN COFFEE dalam pengadaan bahan baku berkualitas.
SmartNutri: An Android-Based Application to Improve Parental Nutrition Literacy and Growth Monitoring for Children Under Five Years Old Kaharu, Nur Alinuddin; Wildan
Data Science: Journal of Computing and Applied Informatics Vol. 10 No. 1 (2026): Data Science: Journal of Computing and Applied Informatics (JoCAI)
Publisher : Talenta Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32734/jocai.v10.i1-23411

Abstract

Stunting and undernutrition among children under five remain major public health challenges in Indonesia, primarily due to low parental nutrition literacy, limited access to educational resources, and the absence of consistent household-level growth monitoring. These issues lead to poor nutritional practices and hinder national efforts to reduce stunting. Addressing these problems requires innovative, evidence-based digital interventions that can simplify complex nutrition information into practical guidance for parents. This study aims to develop and evaluate SmartNutri, an Android-based nutrition education application designed using Object-Oriented Programming (OOP) and the Waterfall development model. The application integrates a nutrition calculator, growth monitoring dashboard, and menu recommendations aligned with the Indonesian Ministry of Health Regulation No. 2 of 2020 and WHO growth standards, forming a comprehensive and user-friendly platform. A mixed-method approach was used, involving requirement analysis from 50 parents and 10 Posyandu health workers, iterative software development, and quantitative usability testing. The system achieved 100% functional accuracy across 25 test cases and an average System Usability Scale (SUS) score of 84.6 (“Excellent”), reflecting high user satisfaction and operational stability. Parental nutrition knowledge significantly increased from 58.4 ± 10.2 to 81.7 ± 8.9 (p < 0.001) after four weeks of use, confirming SmartNutri’s educational effectiveness. SmartNutri successfully bridges the gap between nutrition literacy and behavioral practice, providing a scalable, evidence-based digital tool to support early childhood nutrition. Future research will focus on long-term impact assessment, integration with community health systems, and AI-driven personalization to enhance engagement, scalability, and public health relevance.
Short-Term IHSG Closing Price Prediction Using Random Forest Ayu Hernita; Oki Derajat Sudarmojo; Sabarudin Saputra; Nur Alinuddin Kaharu; Wildan
Information Technology Education Journal Vol. 4, No. 3, August (2025)
Publisher : Jurusan Teknik Informatika dan Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59562/intec.v4i3.9549

Abstract

Predicting stock market prices is challenging due to the complex and volatile nature of financial time series. This study examines the use of Random Forest Regression (RFR) to predict the closing prices of the Jakarta Composite Index (IHSG) from January 2015 to May 2025. Historical data were collected from Yahoo Finance, preprocessed, and engineered into seven predictor features, including lagged prices, moving averages, volatility measures, and a COVID-19 event indicator.The dataset was split into training and testing sets (80:20) using a time-based approach. Hyperparameters were optimized via RandomizedSearchCV with TimeSeriesSplit cross-validation. The final model achieved an RMSE of 177.55 and an R² of 0.71 on the testing set, demonstrating strong predictive performance. Feature importance analysis indicated that the previous day’s closing price (lag_1) was the most influential predictor, followed by lag_2 and MA_7.Visualizations showed that the model effectively captured major trends and turning points, with minor deviations during extreme volatility. The next-day prediction for May 23, 2025, yielded a closing price of 7145.12, indicating practical applicability for short-term investment decisions. The results highlight that Random Forest Regression is a robust and effective method for predicting financial time series, capable of handling non-linear patterns and market fluctuations
Co-Authors Alimuddin Yasin Astari, Kurniati Dwi Ayu Hernita Bonitalia mohammad fadur rahman Oki Derajat Sudarmojo Risaldi, Moh Risaldi, Moh. Romadhona, Agus Sabarudin Saputra Saiful Silviani, Jeane Anastasia Sukardi Sukardi Wildan Wildan Wildan Wildan