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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Jurnal Teknologi Informasi dan Ilmu Komputer Jurnal Transformatika Journal of Information System E-Dimas: Jurnal Pengabdian kepada Masyarakat JOURNAL OF APPLIED INFORMATICS AND COMPUTING Jurnal Pengembangan Rekayasa dan Teknologi Jurnal Surya Masyarakat JSiI (Jurnal Sistem Informasi) JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) ScientiCO : Computer Science and Informatics Journal Journal of Information Systems and Informatics Aiti: Jurnal Teknologi Informasi Jurnal Surya Masyarakat Djtechno: Jurnal Teknologi Informasi Jurnal Akuntansi Dan Bisnis Indonesia (JABISI) Jurnal Informatika Teknologi dan Sains (Jinteks) Jurnal Riptek SEMINAR NASIONAL PENELITIAN DAN PENGABDIAN KEPADA MASYARAKAT Jurnal INFOTEL Tematik Jurnal Informatika: Jurnal Pengembangan IT KETIK : Jurnal Informatika Jurnal Ilmiah Informatika dan Komputer Signal and Image Processing Letters International Journal of Artificial Intelligence and Science Jurnal DIMASTIK
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 Education 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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Implementasi Pemilihan Fitur Metode Wrapper dan Embedded dalam Prediksi Ketepatan Kelulusan Mahasiswa Aria Hendrawan; Lenny Margaretta Huizen; Agusta Praba Ristadi Pinem; Dinar Anggit Wicaksana
Seminar Nasional Penelitian dan Pengabdian Kepada Masyarakat 2021: Prosiding Seminar Nasional Penelitian dan Pengabdian Kepada Masyarakat (SNPPKM 2021)
Publisher : Universitas Harapan Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (272.981 KB)

Abstract

Era Big Data saat ini, kumpulan data memiliki struktur yang sangat beragam serta dimensi yang tinggi. Dimensi tinggi ini memiliki independen atribut, dependen atribut, data yang tidak relevan dan tidak berguna yang menjadi sumber masalah dalam mendapatkan akurasi yang terbaik di proses penerapan metode klasifikasi prediksi. Pemilihan fitur pada setiap kasus klasifikasi prediksi sangat penting untuk dilakukan karena merupakan komponen dalam pembelajaran mesin dan alur kerja penelitian. Dalam penelitian ini, pemilihan fitur atribut data menggunakan metode wrapper dan embedded yang dilakukan pada dataset mahasiswa yang telah menyelesaikan sarjana di perguruan tinggi sebanyak 146 data yang memiliki 13 atribut data dan 1 label. Metode pemilihan fitur ini dilakukan untuk mendapatkan atribut data yang sesuai yang nantinya digunakan dalam proses klasifikasi prediksi mahasiswa yang telah lulus tepat waktu atau tidak. Hasil penelitian menunjukkan dengan menggunakan algoritma Random Forest Classification, nilai akurasinya sebesar 73% dan terjadi peningkatan akurasi setelah menggunakan metode pemilihan fitur wrapper dan embedded sebesar 100%.
Analisis Faktor yang Mempengaruhi Lama Studi Mahasiswa dengan Metode SMARTER Lenny Margaretta Huizen; Aria Hendrawan; Agusta Praba Ristiadi Pinem
AITI Vol 19 No 2 (2022)
Publisher : Fakultas Teknologi Informasi Universitas Kristen Satya Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24246/aiti.v19i2.213-227

Abstract

Salah satu kriteria dalam penilaian akreditasi perguruan tinggi adalah kelulusan mahasiswa tepat waktu. Faktor yang mempengaruhi lama studi mahasiswa perlu dianalisis. Dalam penelitian ini metode SMARTER digunakan untuk menemukan faktor-faktor tersebut. Data diperoleh dari kuesioner dengan 133 responden. Uji validitas dan reliabilitas dilakukan untuk mengetahui kevalidan alat uji. Pengujian dilakukan dengan uji signifikansi 0,05 untuk uji validitas dan uji reliabilitas sebesar 0,811 yang berarti reliabilitasnya sangat tinggi. Data diolah dengan metode SMARTER. Metode SMARTER merupakan metode penyelesaian dimana bobot dihitung menggunakan perhitungan pembobotan Rank Order Centroid (ROC) pada setiap kriteria dan sub kriteria. Data real menunjukkan bahwa faktor dosen merupakan faktor utama yang mempengaruhi lama studi mahasiswa, disusul dengan faktor mahasiswa, faktor sarana dan prasarana, serta faktor dari luar. Dari hasil metode SMARTER diperoleh bahwa faktor yang paling mempengaruhi kelulusan mahasiswa adalah faktor dosen dengan nilai utility sebesar 15,918. Faktor kedua adalah faktor mahasiswa yang mempunyai nilai utility sebesar 8,297, disusul faktor sarana dan prasarana sebesar 4,425, dan faktor dari luar sebesar 1,312. Dari hasil perhitungan metode SMARTER dan data real didapat bahwa faktor bimbingan dosen merupakan faktor yang mempunyai pengaruh besar dalam perhitungan lama studi mahasiswa.
Pemanfaatan Instagram untuk Media Promosi dalam Meningkatkan Ketahanan UMKM bagi Forum Gerakan Terintegrasi Masyarakat Koperasi dan Usaha Mikro (Gerai Kopimi) Lamper Lor Semarang Selatan Aria Hendrawan; Khoirudin Khoirudin; Vensy Vydia
Jurnal Surya Masyarakat Vol 6, No 1 (2023): November 2023
Publisher : Universitas Muhammadiyah Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26714/jsm.6.1.2023.26-30

Abstract

This dedication was motivated by a request from one of the GERAI KOPIMI FORUM (Gerakan Terintegrasi Masyarakat Koperasi Dan Usaha Mikro) located in Lamper Lor, South Semarang, so that Lamper Lor, UMKM South Semarang could reach a wider market.  With the COVID-19 pandemic, product marketing must be transformed using internet media, due to the limited space for product marketing. USM community service team through the Community Service program will provide assistance to the UMKM Forum Gerai Kopimi Semarang Selatan using social media to promote their products more broadly. Armed with information technology knowledge, especially in the field of marketing using social media and product shooting techniques owned by the service team, it is hoped that they will be able to expand the reach of marketing their products. This activity will be carried out in several stages, First, conducting field surveys related to partner problems, Second, designing the most appropriate methods for utilizing social media for product marketing and designing the most suitable packaging designs for products so that they can increase the value of partner products. Third, conduct training for partners to optimize social media in marketing products. Fourth, evaluate the results of the training. The positive response was given by almost all participants who wanted to use the Instagram platform as a medium for promoting products of UMKM and felt satisfied with the training provided, so they wanted more training.
SISTEM PENDUKUNG KEPUTUSAN PEMILIHAN BAHAN BAKU ALUMUNIUM MENGGUNAKAN METODE VISEKRITERIJUMSKO KOMPROMISNO RANGIRANJE Pinem, Agusta Praba Ristadi; Hendrawan, Aria; Sekar Sari, Mega
Jurnal Sistem Informasi Vol 9 No 1 (2022)
Publisher : Universitas Serang Raya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30656/jsii.v9i1.3955

Abstract

Supplier bahan baku yang berkualitas baik, dapat membantu dalam ketrampilan proses pembuatan meubel dalam persaingan penjualan meubel, berdampak pada evaluasi penentuan supplier bahan baku, tidak mengalami kekecewaan dalam memilih barang. Begitu pula dengan Meubel Sulkani Putra Alumunium yang memproduksi dan menjual perabotan meubel rumah tangga. Pemilihan supplier bahan baku, dilakukan oleh pemilik meubel yang masih mengalami kesulitan, dalam menentukan alternatif terbaik, karena begitu banyak kriteria penentu yang di pertimbangkan, serta membutuhkan pengambilan keputusan yang akurat dan tepat sasaran. Permasalah tersebut, Meubel Sulkani Putra Alumunium membutuhkan Sistem Pendukung Keputusan (SPK) dalam menentukan supplier bahan baku. Metode SPK yang digunakan dalam penelitian adalah Vlse Kriterijumska Optimizacija I Kompromisno Resenje (VIKOR). Pada penelitian kali ini kriteria yang digunakan adalah harga barang, waktu pemesanan, respon pemesanan, kualitas, garansi, jatuh tempo dan diameter/ketebalan. Metode ini berfokus pada perangkingan dan memilih dari satu set alternatif, dan menentukan solusi yang dapat membantu para pengambil keputusan untuk mencapai keputusan akhir. Hasil dari penelitian dengan pengujian menggunakan metode korelasi rank spearman menunjukan bahwa metode VIKOR memiliki nilai keeratan 0.8 dengan fakta penilain supplier bahan baku aluminium. Kata kunci: Sistem Pendukung Keputusan, Supplier Bahan Baku, VIKOR
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.
Implementasi Sistem Administrasi di Unit Pelaksana Teknis Pusat Pengembangan Publikasi Ilmiah Dosen Universitas Semarang berbasis Website Pradana, Muhamamd Surya Jati; Hendrawan, Aria; Christioko, B.Very; Pinem, Agusta Praba Ristadi
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 9 No 3: Juni 2022
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.2021864089

Abstract

Pusat Pengembangan Publikasi Ilmiah Dosen (P3ID) ialah Unit Pelaksana Teknis (UPT) yang dibentuk di Universitas Semarang guna melakukan pengelolaan arsip surat yang berkaitan dengan jurnal. Kegiatan pengelolaan dan pengarsipan surat jurnal di P3ID masih dilakukan dengan cara manual, yang memungkinkan terjadinya kesalahan dalam pencatatan surat terlebih bila surat yang diarsipkan jumlahnya banyak. Dengan alasan tersebut, maka penelitian dilakukan untuk mengimplementasikan algoritma Sequential Search dalam proses pencarian arsip surat di P3ID ini. Metode yang digunakan dalam pengembangan sistem ialah metode Rapid Application Development (RAD), metode yang menekankan pada pengembangan sistem dengan jangka waktu yang singkat. Dari hasil implementasi pada sistem Administrasi berbasis Website ini, sistem berjalan dengan baik. Proses pencarian yang dilengkapi dengan algoritma Sequential Search ini juga dapat menampilkan hasil yang dicari berdasarkan kata kunci perihal. AbstractLecturer’s Scientific Publication Development Center is a Technical Implementation Unit established at the University of Semarang to manage the archive of letters related to journals. The management and archiving activities of journal letters in P3ID are still done by manual means, which allows for errors in the recording of letters especially when the letters archived in large numbers. For this reason, research was conducted to implement the Sequential Search algorithm in the process of searching for mail archives in this P3ID. The method used in system development is the Rapid Application Development (RAD) method, a method that emphasizes system development for a short period of time. From the implementation results on this Website-based Administration system, the system is running well. The search process equipped with sequential search algorithm can also display the results searched based on the subject keyword.
Vehicle Detection on The Traffic Using Detection Transformer (DETR) Algorithm Khoiriyah, Rofiatul; Hendrawan, Aria
International Journal of Artificial Intelligence and Science Vol. 1 No. 1 (2024): September
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/IJAIS.v1.i1.4

Abstract

Object detection is a computer vision technique aimed at detecting and identifying objects in images or videos. In recent years, with advancements in Machine Learning and Deep Learning, object detection has made significant progress in various fields such as healthcare, security, and transportation. The DETR algorithm is a novel approach in object detection that combines transformer architecture with attention techniques to address object detection challenges. This research applies the DETR algorithm with ResNet backbone for vehicle detection on the roads, involving 6 object classes: Car, Truck, Bus, Motorcycle, Pickup Car, and Truck Box. Four training experiments were conducted: DETR-ResNet50, DETR-ResNet101, DETR-DC5-ResNet50, and DETR-DC5-ResNet101. The implementation results show that DETR-DC5 improves the accuracy of vehicle detection. DETR-DC5 with ResNet-101 achieved the highest score for AP50, which is 0.957. However, it should be noted that DETR-DC5 with ResNet-50 managed to maintain overall AP stability, with a lower parameter of 35.5. The model's outcomes in this study can be effectively applied for vehicle detection on the roads.
Traffic Vehicle Detection Using Faster R-CNN: A Comparative Analysis of Backbone Architectures Hakim, Luqman; Hendrawan, Aria; Khoiriyah, Rofiatul
International Journal of Artificial Intelligence and Science Vol. 1 No. 1 (2024): September
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/IJAIS.v1.i1.5

Abstract

Object detection is a crucial task in computer vision, where advanced deep learning models have shown significant improvements over traditional methods. In this study, the Faster R-CNN algorithm is applied to a traffic dataset containing six vehicle categories: Bus, Car, Motorcycle, Pick Up Car, Truck, and Truck Box. The novelty of the research lies in the comparison of four backbone architectures ResNet50, ResNet50V2, MobileNetV3 Large, and MobileNetV3 Large 320 evaluated for their performance in vehicle detection at IoU thresholds of 0.5 and 0.75. The results reveal that ResNet50 provided the best overall performance, achieving mAP scores of 0.966 at IoU 0.5 and 0.887 at IoU 0.75, offering a balanced trade-off between precision and recall. ResNet50V2 and MobileNetV3 Large also performed well, with mAP scores of 0.945 and 0.870 for ResNet50V2, and 0.969 and 0.843 for MobileNetV3 Large, respectively. However, MobileNetV3 Large 320 showed the lowest detection performance, with mAP scores of 0.857 at IoU 0.5 and 0.551 at IoU 0.75. These findings provide useful insights into the suitability of different architectures for vehicle detection tasks, particularly in traffic surveillance applications.
Co-Authors Abhinaya Putra Adiuntoro, Alwan Agho, Davit Cany Agusta Praba Ristadi Pinem Agusta Praba Ristadi Pinem Agusta Praba Ristadi Pinem Aprih Santoso April Firman Daru Ardiani Ika Sulistyawati Aria Setiaji Aristo Kevin Ardyaneira Pratama Asmiatun, Siti Astrid Novita Putri, Astrid Atmoko Nugroho Awaliyah, Rafika Farkhul Basworo Ardi Pramono Charis Maulana Cholil, Saifur Rohman Christine Dewi Christioko, B.Very Davit Cany Agho Dimas Dwi Budiarjo Febrian Wahyu Christanto Hasna Rizqia Khairunnisa Henny Indriyawati Jihan Kusumawardhani Jovita Wayan Ramadhani Khoiriyah, Rofiatul Khoirudin Khoirudin Lenny Margaretta Huizen Luqman Hakim Maulida, Innayah Maulifa Sekar Arum Nabilah Putri Nur Wakhidah Oky Dwi Nurhayati Pradana, Muhamamd Surya Jati Prind Triajeng Pungkasanti, Prind Triajeng Pulung Nurtantio Andono Purwanto Purwanto Putri, Maulina Rahmat Gernowo Rizky Haryanto Putro Saputri, Anita Nur Widdia Satria Dava Riansa Sekar Sari, Mega Setiaji, Aria Soiful Hadi Supari Supari, Supari Susanto, Susanto Toti Kresna Wardana Vensy Vydia Whisnumurti Adhiwibowo Wicaksana, Dinar Anggit Winarti, Titin