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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Jurnal Teknologi Informasi dan Ilmu Komputer Jurnal Transformatika E-Dimas: Jurnal Pengabdian kepada Masyarakat Jurnal Pengembangan Rekayasa dan Teknologi Jurnal Surya Masyarakat JSiI (Jurnal Sistem Informasi) ScientiCO : Computer Science and Informatics Journal Journal of Information Systems and Informatics Aiti: Jurnal Teknologi Informasi Jurnal Surya Masyarakat Jurnal Riptek SEMINAR NASIONAL PENELITIAN DAN PENGABDIAN KEPADA MASYARAKAT Jurnal INFOTEL Tematik Jurnal Informatika: Jurnal Pengembangan IT Signal and Image Processing Letters
Aria Hendrawan, Aria
Universitas Semarang
Religion Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Mechanical Engineering Nursing Public Health Social Sciences Transportation Other

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Artificial Neural Network for Corn Quality Classification Based on Seed Damage and Aflatoxin Attributes Maulida, Innayah; Hendrawan, Aria; Khoiriyah, Rofiatul
Signal and Image Processing Letters Vol 7, No 1 (2025)
Publisher : Association for Scientific Computing Electrical and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/simple.v7i1.119

Abstract

Corn plays a critical role in Indonesia’s agricultural sector, functioning as both a staple food for human consumption and a key component of livestock feed. However, its quality is frequently compromised by factors such as mechanical damage during harvesting, fungal contamination, and fluctuating climate conditions, all of which pose challenges to maintaining consistent standards. Traditionally, corn quality classification relies on manual methods, which are not only time-consuming but also prone to human error and inconsistency. To address these limitations, this study employs a Neural Network approach to classify corn into two distinct categories: breeder and commercial grades. The research utilizes a dataset of 2,026 records, meticulously divided into 70% for training, 20% for validation, and 10% for testing, ensuring robust model evaluation. The methodology includes comprehensive data preprocessing, feature standardization to normalize input variables, and hyperparameter optimization, with the model trained over 100 epochs using a batch size of 32 and a learning rate of 0.001. The results demonstrate exceptional performance, achieving an accuracy of 99.5%, precision of 98.3%, recall of 100%, and an F1-score of 99.1%, as validated by a confusion matrix that highlights the model’s classification reliability. This automated system significantly enhances the efficiency and accuracy of corn quality assessment, offering a scalable solution to replace outdated manual techniques. By providing a reliable tool for quality differentiation, this study supports Indonesia’s agricultural and livestock industries, with potential for broader application in optimizing crop management and ensuring food security under varying environmental conditions.
Klasifikasi Pertanyaan Quora Menggunakan Metode Keyword-based dan Analisis Sentimen dengan ComplementNB Adiuntoro, Alwan; Hendrawan, Aria
Jurnal Informatika: Jurnal Pengembangan IT Vol 10, No 2 (2025)
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v10i2.7965

Abstract

Text classification is a fundamental task in Natural Language Processing (NLP) that supports the categorization of data based on predefined labels. This study aims to evaluate the effectiveness of keyword-based labeling and sentiment analysis methods for text classification using the Quora Questions dataset. The dataset comprises 16,921 samples with imbalanced class distribution, where the opinion category dominates, while the hypothetical category is a minority class. The labeling process utilized a keyword-based approach for the fact and hypothetical categories, while the opinion category was labeled using sentiment analysis with the Vader Lexicon library. TF-IDF was employed as the feature representation method, with two approaches explored: n-gram range tuning (1–3) and without tuning. ComplementNB, designed for handling imbalanced datasets, was utilized for classification, with a training-test split of 70:30. The results show that the approach without n-gram tuning achieved the highest accuracy of 93.89%, with zero variance in cross-validation. Evaluation revealed that ComplementNB effectively handles class imbalance, as demonstrated by high precision and recall in the minority class. This study demonstrates that a simple approach combining keyword-based labeling and sentiment analysis can be effectively implemented for category-based text classification tasks, particularly in platforms like Quora. These findings are relevant for similar applications requiring real-time text classification with minimal complexity.
Vehicle Detection and Tracking using Coarse-to-Fine Module and Spatial Pyramid Pooling–Fast with Deep Sort Saputri, Anita Nur Widdia; Hendrawan, Aria; Khoiriyah, Rofiatul
Signal and Image Processing Letters Vol 7, No 2 (2025)
Publisher : Association for Scientific Computing Electrical and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/simple.v7i2.118

Abstract

Semarang City, a rapidly growing urban area in Indonesia, faces significant traffic challenges stemming from the widespread use of motorcycles, an inefficient public transportation system, and accelerated urban development. These factors contribute to congestion and complicate traffic management efforts. To address this issue and enhance monitoring capabilities, this study develops an automatic vehicle detection system utilizing the YOLOv8 algorithm, applied to CCTV footage obtained from TILIK SEMAR, a local traffic surveillance initiative. The research methodology encompasses several key stages: data collection from real-world traffic scenarios, meticulous annotation of vehicle types, model training using the YOLOv8 framework, and performance evaluation conducted at two distinct locations in Semarang—Banyumanik and Thamrin Pandanaran. The trained model achieved an impressive average accuracy, measured as mean Average Precision (mAP50), exceeding 97%, with a rapid processing time of 4.2 milliseconds per image, making it suitable for real-time applications. Among vehicle categories, the highest detection accuracies were recorded for buses at 99.3% and box trucks at 99.5%, reflecting the model’s robustness for larger vehicles. However, motorcycles presented a challenge, with a lower mAP50-95 score of 64.3%, attributed to variations in shape, size, and lighting conditions. Overall, the system successfully identified 96.77% of 3,036 vehicles across the test dataset, demonstrating strong generalization across diverse traffic conditions. These findings validate YOLOv8 as an effective tool for real-time traffic monitoring in urban settings. Future enhancements will focus on expanding dataset diversity and improving performance under challenging environmental factors, such as adverse weather or low-light scenarios, to further refine the system’s reliability.
The Comparative Analysis Of Multi-Criteria Decision-Making Methods (MCDM) In Priorities Of Industrial Location Development Pinem, Agusta Praba Ristadi; Hendrawan, Aria; Wakhidah, Nur
JURNAL INFOTEL Vol 16 No 4 (2024): November 2024
Publisher : LPPM INSTITUT TEKNOLOGI TELKOM PURWOKERTO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/infotel.v16i4.1099

Abstract

The process of prioritizing the development of an industrial area's site is a matter that necessitates a mature approach. The establishment of an industrial region has significant social implications for the surrounding locality. However, it is also necessary to take into account the availability of variables that facilitate the functioning of such an industrial zone. The goal of the study "A Comparative Analysis of Multi-Criteria Decision Making Methods (MCDM) for Determining the Priority of Industrial Area Location Development" is to compare and contrast different MCDM methods in the context of deciding which industrial area locations should be developed first. A case study was undertaken, examining various possible industrial sites for future development. Multiple approaches, namely MOORA, WASPAS, ARAS, COPRAS, and AHP, are employed to ascertain the prioritization of industrial area development locations. This study presents a comparative analysis of each approach by using the Spearman Rank correlation and utilizing the factual data obtained from the Department of Capital Plantation and Integrated One Door Services (DPMPTSP). The external research is anticipated to involve a comprehensive review of the literature on the efficacy of Multiple Criteria Decision Making (MCDM) methods. This research has the potential to assist both governmental bodies and private entities in establishing priorities for the development of industrial areas, taking into account prevailing circumstances and conditions while also considering various significant factors and criteria.
RASPBERRY PI DENGAN MODUL KAMERA DAN MOTION SENSOR SEBAGAI SOLUSI CCTV LAB FTIK UNIV. SEMARANG Pramono, Basworo Ardi; Hendrawan, Aria; Daru, April Firman
Jurnal Pengembangan Rekayasa dan Teknologi Vol. 2 No. 1 (2018): Mei (2018)
Publisher : Universitas Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26623/jprt.v14i1.1213

Abstract

Pengawasan tempat/ruangan dalam pekerjaan merupakan hal   penting, dimana dengan adanya CCTV pemantau kita bisa melihat kondisi suatu tempat dengan bantuan kamera pemantau (CCTV). Selama ini untuk perekaman CCTV mengunakan DVR (Digital Video Recording) dimana perangkat ini cenderung mahal dan belum menjangkau semua kalangan. Terlebih lagi jika CCTV merekam secara terus menerus 24 jam sehari selama 1 bulan, tentu harus menyediakan kapasitas ruang harddisk yang besar.Ruang Lab FTIK menjadi objek penelitian dimana mengunakan perangkat raspberry pi dengan modul kamera dan sensor motion detection dimana perangkat PC Mini Raspberry   Pi hanya akan merekam kondisi ruangan hanya pada saat terdeteksi suatu gerakan pada ruang Lab FTIK. Raspberry pi sendiri adalah sebuah komputer mini, sistem operasi Raspberry bisa bermacam-macam, salah satunya adalah Linux Debian yang telah dipaket minikan. Dengan itu diharapkan mampu mengurangi beban media penyimpanan. Penggunaan Raspberry Pi dalam hal pengawasan/monitoring tempat atau ruangan   memerlukan biaya yang murah dan efektif dalam pendayagunaan.
PEMODELAN PENENTUAN KREDIT SIMPAN PINJAM MENGGUNAKAN METODE ADDITIVE RATIO ASSESSMENT (ARAS) Maulana, Charis; Hendrawan, Aria; Pinem, Agusta Praba Ristadi
Jurnal Pengembangan Rekayasa dan Teknologi Vol. 3 No. 1 (2019): Mei (2019)
Publisher : Universitas Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26623/jprt.v15i1.1483

Abstract

Beberapa koperasi dalam memberikan pinjaman ke anggotanya sangat bergantung pada masing-masing pemberi keputusan dan bobot penilaian yang berbeda untuk setiap kriteria. Berbeda dengan pinjaman di bank, pinjaman pada koperasi memiliki kriteria yang mengacu pada aturan tiap koperasi. Hal ini menjadi menarik untuk dilakukan penelitian dengan menerapkan metode Additive Ratio Assessment dalam suatu sistem pendukung keputusan, sehingga dapat membantu dalam menentukan penerima pinjaman koperasi untuk menghindari kredit macet. Sistem Pendukung Keputusan (SPK) adalah sistem yang dapat membantu seseorang, dalam mengambil suatu keputusan yang akurat dan tepat sasaran. Banyak permasalahan yang dapat diselesaikan dengan menggunakan SPK, contohnya membangun model sistem pendukung keputusan penentuan anggota koperasi potensial dalam pengajuan pinjaman untuk menghasilkan informasi anggota koperasi potensial untuk menghindari kredit macet.  
LSTM Forecasting and K-Means Clustering for Passenger Mobility Management at Bus Terminals Khairunnisa, Hasna Rizqia; Hendrawan, Aria
Journal of Information System and Informatics Vol 7 No 3 (2025): September
Publisher : Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v7i3.1159

Abstract

Rapid urban population growth has increased the need for efficient public transportation systems, particularly at bus terminals as major mobility hubs. To address operational challenges such as traffic congestion and limited infrastructure, this study proposes an innovative data-driven approach. A hybrid model is applied, integrating Long Short-Term Memory (LSTM) for passenger volume forecasting and K-Means Clustering for mobility pattern segmentation at the Jepara Bus Terminal. Monthly passenger data was utilized, and the K-Means method was applied to group monthly mobility patterns into three categories: low, medium, and high. The optimal cluster selection (k=3) was based on the highest Silhouette score of 0.785, providing clear seasonal insights. Analysis results indicate that September is the peak mobility period, while months like January and February fall into the low category. Furthermore, an LSTM model was trained to predict future passenger volumes. The model's performance was carefully validated and proven accurate, with a Mean Squared Error (MSE) of 0.0304 and a Root Mean Squared Error (RMSE) of 0.1745. These findings confirm that the model is reliable in capturing complex passenger movement patterns. Overall, this study concludes that the combination of LSTM and K-Means is an effective solution for supporting proactive decision-making. The results of this study can assist terminal managers in optimizing resource allocation and formulating more adaptive operational strategies, thereby contributing to the development of a more responsive and efficient intelligent transportation system.
Co-Authors Adiuntoro, Alwan Agusta Praba Ristadi Pinem April Firman Daru Basworo Ardi Pramono Cholil, Saifur Rohman Christine Dewi Dimas Dwi Budiarjo Febrian Wahyu Christanto Henny Indriyawati Huizen, Lenny Margaretta Khairunnisa, Hasna Rizqia Khoiriyah, Rofiatul Khoirudin Khoirudin Khoirudin Khoirudin, Khoirudin Maulana, Charis Maulida, Innayah Muhamamd Surya Jati Pradana Nur Wakhidah Oky Dwi Nurhayati Pinem, Agusta Praba Ristadi Prind Triajeng Pungkasanti, Prind Triajeng Pulung Nurtantio Andono Rahmat Gernowo Saifur Rohman Cholil Saputri, Anita Nur Widdia Sekar Sari, Mega Siti Asmiatun, Siti Soiful Hadi Susanto Susanto Titin Winarti Vensy Vydia Whisnumurti Adhiwibowo Wicaksana, Dinar Anggit