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All Journal Jurnal Teknologi Informasi dan Ilmu Komputer 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
Arsitektur Mesh 6G-IoT Menggunakan Intelligent Reflecting Surfaces (IRS) untuk Optimasi Sinyal di Area yang Sulit Dijangkau Wildan; Moh. Risaldi; Nur Alinuddin Kaharu; Agus Romadhona; Sukardi
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 13 No 2: April 2026
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.132

Abstract

Perkembangan jaringan 6G diproyeksikan menjadi fondasi utama bagi ekosistem Internet of Things (IoT) berskala masif dengan kebutuhan cakupan yang luas, latensi sangat rendah, serta efisiensi energi tinggi. Namun, area sulit jangkau seperti pedesaan terpencil, daerah pegunungan, atau wilayah dengan banyak halangan fisik masih menjadi tantangan utama bagi operator jaringan. Di sisi lain, arsitektur jaringan mesh dan teknologi Intelligent Reflecting Surfaces (IRS) muncul sebagai kandidat kuat untuk membentuk lingkungan radio yang dapat diprogram dan memperluas jangkauan sinyal tanpa menambah daya pancar secara signifikan. Artikel ini mengusulkan sebuah arsitektur mesh 6G-IoT berbasis IRS yang dirancang khusus untuk mengoptimasi kualitas sinyal di area sulit jangkau. Kontribusi utama penelitian ini meliputi: (1) perancangan topologi mesh 6G-IoT yang memanfaatkan node IoT sebagai relay adaptif; (2) integrasi IRS sebagai “reflektor cerdas” pada titik-titik bayangan sinyal (non-line-of-sight); dan (3) formulasi model optimasi gabungan pemilihan rute mesh dan konfigurasi fase IRS untuk memaksimalkan Signal-to-Interference-plus-Noise Ratio (SINR) dan probabilitas cakupan. Hasil simulasi konseptual menunjukkan bahwa arsitektur yang diusulkan mampu meningkatkan SINR rata-rata hingga sekitar 35% dan cakupan area hingga 30% dibandingkan skema mesh tanpa IRS, serta lebih efisien energi dibandingkan pendekatan penambahan base station konvensional. Penelitian ini mengindikasikan bahwa kombinasi mesh 6G-IoT + IRS berpotensi menjadi solusi praktis untuk memperkecil kesenjangan digital di area sulit dijangkau.   Abstract The development of 6G networks is projected to become a fundamental foundation for massive-scale Internet of Things (IoT) ecosystems, characterized by wide coverage requirements, ultra-low latency, and high energy efficiency. However, hard-to-reach areas such as remote rural regions, mountainous terrains, and environments with significant physical obstructions remain major challenges for network operators. Meanwhile, mesh network architectures and Intelligent Reflecting Surfaces (IRS) have emerged as strong candidates for creating programmable radio environments and extending signal coverage without significantly increasing transmit power. This article proposes an IRS-assisted 6G-IoT mesh architecture specifically designed to optimize signal quality in hard-to-reach areas. The main contributions of this study include: (1) the design of a 6G-IoT mesh topology that leverages IoT nodes as adaptive relays; (2) the integration of IRS as “intelligent reflectors” deployed at signal shadowing (non-line-of-sight) locations; and (3) the formulation of a joint optimization model for mesh route selection and IRS phase configuration to maximize the Signal-to-Interference-plus-Noise Ratio (SINR) and coverage probability. Conceptual simulation results demonstrate that the proposed architecture can improve average SINR by approximately 35% and area coverage by up to 30% compared to conventional mesh schemes without IRS, while also achieving higher energy efficiency than traditional base station densification approaches. These findings indicate that the combination of 6G-IoT mesh networks and IRS has strong potential as a practical solution to reduce the digital divide in hard-to-reach areas.
Co-Authors Agus Romadhona Alimuddin Yasin Astari, Kurniati Dwi Ayu Hernita Bonitalia Moh. Risaldi mohammad fadur rahman Oki Derajat Sudarmojo Risaldi, Moh Risaldi, Moh. Romadhona, Agus Sabarudin Saputra Saiful Silviani, Jeane Anastasia Sukardi Sukardi Sukardi Wildan Wildan Wildan Wildan Wildan