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All Journal JOIV : International Journal on Informatics Visualization SemanTIK : Teknik Informasi Infotech: Jurnal Informatika & Teknologi Jurnal Ilmiah Sistem Informasi dan Teknik Informatika (JISTI) Adopsi Teknologi dan Sistem Informasi
Muhammad Abdillah Rahmat
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Energy Engineering Industrial & Manufacturing Engineering

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PEMILIHAN LOKASI BARU BTS TELKOMSEL CABANG KOTA KENDARI MENGGUNAKAN METODE SAW DAN TOPSIS BERBASIS WEB GIS Muhammad Abdillah Rahmat; Bambang Pramono; Rizal Adi Saputra
semanTIK Vol 3, No 1 (2017): semanTIK
Publisher : Informatics Engineering Department of Halu Oleo University

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (280.621 KB) | DOI: 10.55679/semantik.v3i1.2590

Abstract

Komunikasi adalah proses interaksi seseorang atau kelompok dalam menyampaikan informasi yang berupa pesan, ide dan gagasan dari satu pihak ke pihak lain.  Hal ini yang mendorong manusia untuk berinovasi dalam menciptakan teknologi baru, salah satunya adalah telekomunikasi GSM (Global System for Mobile Communication).Kota Kendari adalah salah satu kota di Indonesia yang penduduknya banyak menggunakan brand GSM Telkomsel.Untuk mengatasi masalah tersebut diperlukan suatu aplikasi yang dapat diakses dan mempermudah Telkomsel dalam menetapkan lokasi pembangunan BTS yang baru dengan berbasis Web  Geographic Information System (GIS) agar mempermudah pemetaan lokasi baru yang layak untuk dibangun sebuah BTS dan dapat berlaku secara user friendly terhadap pengguna aplikasi.Hasil penelitian ini menunjukkan metode SAW dan TOPSIS adalah dua metode yang dapat diterapkan dalam  pencarian lokasi baru, karena metode SAW dan TOPSIS dapat menghasilkan sistem pendukung keputusan yang lebih baik dibandingkan menggunakan satu metode diantaranya.Kata kunci— Komunikasi, BTS, SAW, TOPSIS.
Analisis Kinerja Algoritma YOLO dalam Penghitungan Benih Udang Siska Armalivia; Muhammad Abdillah Rahmat; Thiara Tri Funny Manguma; Rahmawati
Jurnal Ilmiah Sistem Informasi dan Teknik Informatika (JISTI) Vol 7 No 1 (2024): Jurnal Ilmiah Sistem Informasi dan Teknik Informatika (JISTI)
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat Universitas Lamappapoleonro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57093/jisti.v7i1.202

Abstract

The two main processes in shrimp cultivation are filling and enlargement. The counting of shrimp seeds is part of the filling activity. However, the counting of shrimp after larvae is still done manually, which means taking the shrimp and counting them manually, which is time-consuming and often results in human error. This research proposes a You Only Look Once (YOLO) method to automatically calculate the number of shrimp larvae. The YOLO method is a deep learning model that can detect an object with high speed and accuracy, even under less ideal lighting conditions. In this study, images of objects were taken using a camera placed on top of a white container containing 2 cm of water and photographed with a backlight system to avoid the reflection of light from inside the water. Testing is done by comparing system calculations and manual calculations. The results showed that the system not only detected larvae but also counted the number of shrimp larvae as well as the mAP validation value on the final model built with YOLO, which was 96.83% It also produced an average accuracy with 30 training data of 76.48%.
Diagnosa awal gangguan kesehatan mental mahasiswa tingkat akhir Universitas Almarisah Madani menggunakan metode forward chaining Siska Armalivia; Muhammad Abdillah Rahmat; Putu Cinta Ananda Widyanti; Siti Nuraisyah
INFOTECH : Jurnal Informatika & Teknologi Vol 5 No 2 (2024): INFOTECH: Jurnal Informatika & Teknologi (In Progress)
Publisher : LPPMPK - Sekolah Tinggi Teknologi Muhammadiyah Cileungsi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37373/infotech.v5i2.1402

Abstract

Penelitian ini mengkaji gangguan kesehatan mental di kalangan mahasiswa tingkat akhir di Universitas Almarisah Madani. Meskipun gangguan kesehatan mental dapat berdampak signifikan pada kehidupan individu, masih sering diabaikan dan dianggap tabu. Data menunjukkan ketidakseimbangan antara jumlah penderita gangguan jiwa dan ketersediaan tenaga profesional kesehatan mental di Indonesia. Fokus penelitian adalah pada mahasiswa tingkat akhir Universitas Almarisah Madani yang rentan mengalami tekanan dan stres tinggi akibat tuntutan akademik seperti tugas akhir, dan persiapan memasuki dunia kerja. Tujuan utama penelitian adalah mengidentifikasi gejala awal gangguan kesehatan mental, khususnya depresi dan kecemasan, serta meningkatkan kesadaran akan pentingnya kesehatan mental di kalangan mahasiswa. Metode Forward Chaining digunakan untuk menganalisis gejala yang dilaporkan oleh mahasiswa dan menghasilkan kesimpulan berdasarkan aturan yang telah ditetapkan. Hasil pengujian menunjukkan bahwa sistem berhasil mengidentifikasi gejala awal depresi, kecemasan dan stress dengan baik, memberikan hasil yang akurat dan relevan bagi mahasiswa. Hasil ini juga menunjukkan bahwa metode Forward Chaining yang digunakan efektif dalam mengkaji pola gejala pada responden, dan berpotensi dikembangkan lebih lanjut untuk mendeteksi gangguan mental lainnya
A Thorough Review of Vehicle Detection and Distance Estimation Using Deep Learning in Autonomous Cars Rahmat, Muhammad Abdillah; Indrabayu, Indrabayu; Achmad, Andani; Salam, Andi Ejah Umraeni
JOIV : International Journal on Informatics Visualization Vol 8, No 4 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.4.2665

Abstract

Autonomous vehicle technologies are rapidly advancing, and one key factor contributing to this progress is the enhanced precision in vehicle detection and distance calculation. Deep Learning Networks (DLNs) have emerged as powerful tools to address this challenge, offering remarkable capabilities in accurately detecting and estimating vehicle positions. This study comprehensively reviews DLN applications for vehicle detection and distance estimation. It examines prominent DLN models such as YOLO, R-CNN, and SSD, evaluating their performance on widely used datasets such as KITTI, PASCAL VOC, and COCO. Analysis results indicate that YOLOv5, developed by Farid et al. achieves the highest accuracy level with a mAP (mean Average Precision) of 99.92%. Yang et al. showcased that YOLOv5 performs exceptionally in detection and distance estimation tasks, with a mAP of 96.4% and a low mean relative error (MRE) of 10.81% for distance estimation. These achievements highlight the potential of DLNs to enhance the accuracy and reliability of vehicle detection systems in autonomous vehicles. The study also emphasizes the importance of backbone architectures like DarkNet 53 and ResNet in determining model efficiency. The choice of the appropriate model depends on the specific task requirements, with some models prioritizing real-time detection and others prioritizing accuracy. In conclusion, developing DLN-based methods is crucial in advancing autonomous vehicle technology. Research and development remain crucial in ensuring road safety and efficiency as autonomous vehicles become more common in transportation systems.
Penerapan Model BERT pada Chatbot dalam Platform E-Commerce Rahmat, Muhammad Abdillah; Karmila; Khatami, Husain Muhammad; Muhammad Alief Fahdal Imran Oemar
Adopsi Teknologi dan Sistem Informasi (ATASI) Vol. 4 No. 1 (2025): Adopsi Teknologi dan Sistem Informasi (ATASI)
Publisher : Mulawarman University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30872/atasi.v4i1.3039

Abstract

This study develops chatshop, a BERT-based e-commerce chatbot designed to assist customers in searching for pizza menus. The BERT model was chosen because of its ability to understand sentence context bidirectionally, thereby increasing the accuracy in detecting user intent. This chatshop allows users to find menus, get recommendations, and access price and stock availability information in real time. The evaluation was carried out using BLEU and ROUGE-L metrics, with a ROUGE-L F1 score of 32.91%. These results indicate that the chatbot is able to handle simple interactions well, but still needs improvement in answering more complex questions accurately and completely.
A Cascading of YOLOv8 and Random Forest Regression in Oil Palm Fresh Fruit Bunch Mass Estimation System using Unmanned Aerial Vehicle Imagery Indrabayu, -; Nurhadi, Muhammad Ijlal; Tandungan, Sofyan; Rahmat, Muhammad Abdillah
JOIV : International Journal on Informatics Visualization Vol 9, No 6 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.6.3137

Abstract

Efficient management of oil palm farms requires accurate pre-harvest planning to maximize productivity. Traditional methods for estimating the mass of Fresh Fruit Bunches (FFBs) typically involve manual sampling and weighing, which are time-consuming and prone to errors. This study presents a novel system combining unmanned aerial vehicle (UAV) photography with geometric feature extraction using YOLOv8-Segmentation and machine learning models—Random Forest Regression (RFR)—to estimate FFB mass. The system addresses challenges posed by dynamic drone imagery, including environmental variations and frond occlusions. Instead of directly integrating YOLOv8 with the regression models, geometric features such as the minor axis, perimeter, and eccentricity are extracted from the segmented images and used to train the RFR for mass estimation. The top-performing model, using features extracted from YOLOv8-Small-Segmentation with the minor axis and eccentricity, achieved a Root Mean Square Error (RMSE) of 3.95 and a Mean Absolute Error (MAE) of 2.87 for frond-covered FFBs. For frond-uncovered FFBs, the best-performing features were the minor axis, perimeter, and area extracted using YOLOv8-Large-Segmentation, resulting in an RMSE of 3.91 and MAE of 2.91. These results demonstrate the system's capability to accurately estimate FFB mass based on UAV-captured imagery and feature extraction. This approach offers a scalable and efficient solution for pre-harvest planning in oil palm plantations, addressing the limitations of traditional methods while improving operational efficiency and accuracy in yield estimation.
Co-Authors Andani Achmad Bambang Pramono Indrabayu Indrabayu Indrabayu, - Karmila Khatami, Husain Muhammad Muhammad Alief Fahdal Imran Oemar Nurhadi, Muhammad Ijlal Putu Cinta Ananda Widyanti Rahmawati Rizal Adi Saputra Salam, Andi Ejah Umraeni Siska Armalivia Siti Nuraisyah Tandungan, Sofyan Thiara Tri Funny Manguma