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All Journal TIN: TERAPAN INFORMATIKA NUSANTARA Jurnal Media Informatika Instal : Jurnal Komputer Jurnal Ilmiah Sistem Informasi dan Teknik Informatika (JISTI) Journal of Artificial Intelligence and Digital Business West Science Interdisciplinary Studies West Science Interdisciplinary Studies Journal of Biomedical and Techno Nanomaterials
Justam, Justam
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Penerapan Algoritma C4.5 dan Random Forest untuk Pemetaan Kerusakan Jalan dengan WebGIS Justam, Justam; Jamilah, Nur; Umar, Sitti Mawaddah; Erlita, Erlita; Ramba, Jousadrah
Jurnal Ilmiah Sistem Informasi dan Teknik Informatika (JISTI) Vol 7 No 2 (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.v7i2.270

Abstract

Kerusakan jalan di wilayah Luwu Raya menjadi tanggung jawab BBPJN VI Makassar, yang melakukan pemantauan kondisi jalan dan melaporkan hasilnya untuk tindakan perbaikan. Penelitian ini bertujuan untuk membandingkan algoritma C4.5 dan Random Forest dalam memprediksi prioritas perbaikan jalan dan persebaran kerusakan jalan. Data yang digunakan dalam penelitian ini adalah 6100 data kerusakan jalan pada tiga ruas jalan dari tahun 2021 hingga 2023. Hasil penelitian menunjukkan bahwa meskipun akurasi antara kedua algoritma hampir sama, algoritma Random Forest memberikan hasil yang lebih konsisten dan lebih baik dibandingkan C4.5. Dengan menggunakan algoritma C4.5, didapatkan nilai presisi sebesar 87,9%, recall 82,6%, f1-score 87,8%, dan akurasi 88%. Sementara itu, Random Forest menghasilkan presisi 86,6%, recall 86,8%, f1-score 86,6%, dan akurasi 87%. Penelitian ini menghasilkan sistem informasi pemetaan berbasis WebGIS yang digunakan untuk menentukan prioritas perbaikan jalan. Hasil penelitian ini menunjukkan bahwa Random Forest lebih efektif dalam memprediksi dan menentukan prioritas perbaikan jalan di wilayah Luwu Raya
Sistem Identifikasi Kesegaran Ikan Berbasis Android Menggunakan Convolutional Neural Network (CNN) Justam, Justam; Takbir, Muh. Nashir; Umar, Sitti Mawaddah; Erlita, Erlita; Lewa, Revah Oktria
Jurnal Ilmiah Sistem Informasi dan Teknik Informatika (JISTI) Vol 7 No 2 (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.v7i2.271

Abstract

Ikan merupakan sumber protein hewani penting dengan kandungan vitamin dan mineral esensial yang tinggi. Sebagai negara kepulauan, Indonesia memiliki potensi perikanan yang besar, namun masih banyak masyarakat yang kesulitan membedakan ikan segar dan tidak segar. Penelitian ini mengembangkan sistem berbasis Android untuk mengidentifikasi kesegaran ikan menggunakan Convolutional Neural Network (CNN). Model dilatih dengan 540 sampel gambar dalam tiga kategori (segar, baik, dan tidak layak) dengan resolusi 256 × 256 piksel RGB. CNN yang digunakan terdiri dari tiga lapisan konvolusi dan dua fully connected layer, dengan optimizer Adam dan fungsi aktivasi ReLU serta Softmax. Model dilatih di Google Colaboratory, lalu dikonversi ke TensorFlow Lite untuk diterapkan pada Android. Hasil pengujian menunjukkan akurasi 98% pada data uji dan 96,67% pada aplikasi Android dengan 60 sampel baru, membuktikan sistem mampu berfungsi dengan baik dalam mengidentifikasi kesegaran ikan
IoT dan Pengolahan Citra untuk Sistem Pakan Otomatis Udang dalam Kolam Bioflok Justam, Justam; Hasanuddin, M; Mubarak, Husni; Rahmah, Rahmah; Nurjanah, Titin
Jurnal Ilmiah Sistem Informasi dan Teknik Informatika (JISTI) Vol 7 No 2 (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.v7i2.272

Abstract

Pemberian pakan dalam budidaya udang vaname sangat penting karena mempengaruhi pertumbuhan udang. Saat ini, pengukuran berat rata-rata udang untuk menentukan jumlah pakan dilakukan secara manual, yang memakan waktu dan meningkatkan biaya operasional. Oleh karena itu, diperlukan sistem yang dapat mengestimasi berat rata-rata udang secara otomatis. Penelitian ini mengimplementasikan teknologi pengolahan citra digital untuk mengestimasi berat rata-rata udang vaname berdasarkan citra udang yang diambil. Fitur-fitur citra, seperti jumlah piksel, digunakan untuk estimasi berat rata-rata udang. Hasil penelitian menunjukkan bahwa regresi linier dengan Mean Absolute Error (MAE) sebesar 0,7752 dan regresi polinomial dengan MAE sebesar 0,6869 dapat digunakan untuk estimasi berat udang vaname. Selain itu, penggunaan metode Mask R-CNN terbukti efektif dalam mendeteksi dan menghitung jumlah udang dengan MAE sebesar 0,373. Penelitian ini juga menunjukkan bahwa pemberian pakan sesuai takaran yang dihitung berdasarkan estimasi berat udang dapat meningkatkan efisiensi dalam budidaya udang vaname. Sistem ini memberikan solusi cepat dan akurat untuk memperbaiki proses pemberian pakan, mengurangi biaya operasional, serta mengurangi risiko cedera bagi pembudidaya
Perbandingan Kinerja YOLO vs Faster R-CNN untuk Deteksi & Estimasi Berat Ikan Justam, Justam; Malik, Abdul; Erlita, Erlita; Mangellak, Deo; Yuyun, Yuyun
Jurnal Ilmiah Sistem Informasi dan Teknik Informatika (JISTI) Vol 7 No 2 (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.v7i2.273

Abstract

Ikan kerapu dan ikan kakap memiliki nilai ekonomi tinggi di pasar global, sehingga identifikasi jenis dan estimasi beratnya menjadi aspek penting dalam perdagangan. Metode manual yang umum digunakan memerlukan waktu lama dan tenaga kerja besar. Oleh karena itu, penelitian ini membandingkan performa dua model deep learning, yaitu YOLO dan Faster R-CNN, dalam mendeteksi jenis dan mengestimasi berat ikan. Dataset terdiri dari 2.991 citra yang terbagi dalam 18 kelas dan diperluas melalui augmentasi menjadi 6.843 citra. Proses deteksi menggunakan detection threshold 0,8, dengan evaluasi berdasarkan precision, recall, accuracy, serta Mean Absolute Percentage Error (MAPE) untuk estimasi berat. Hasil menunjukkan bahwa model YOLO memiliki precision, recall, dan accuracy masing-masing sebesar 0,98, 0,98, dan 0,96, sedangkan Faster R-CNN mencapai 0,97, 0,98, dan 0,95. Untuk estimasi berat, MAPE YOLO pada citra sebesar 2,42% dan pada video 3,66%, sementara Faster R-CNN memiliki MAPE 14,62% pada citra dan 13,59% pada video. Dengan demikian, model YOLO menunjukkan kinerja lebih baik dibandingkan Faster R-CNN dalam mendeteksi jenis dan mengestimasi berat ikan
Development of a Student Attendance System Based on Face Recognition at SMKN 1 Luwu Utara Malik, Abdul; Justam; M. Hasanuddin; Kurniawan, Fahmi; Ardiansyah, Muh.; Fitri, Nurul
Bahasa Indonesia Vol 15 No 02 (2023): Instal : Jurnal Komputer Periode (Juli-Desember)
Publisher : Cattleya Darmaya Fortuna

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54209/jurnalinstall.v15i02.339

Abstract

The face recognition-based attendance system is an innovative solution to improve the efficiency and accuracy of student attendance at SMKN 1 Luwu Utara. This study aims to develop and implement a Face Recognition-based attendance system using the Convolutional Neural Network (CNN) algorithm. The research method used is an experimental method with stages of needs analysis, system design, implementation, and system testing. The results show that this attendance system has an accuracy rate of up to 95% in recognizing students' faces, thus reducing the risk of attendance fraud. Additionally, the system provides a seamless and automated way of tracking attendance, eliminating manual errors and reducing administrative workload. The implementation of this system has also received positive responses from the school due to its ease of use and effectiveness in recording attendance data in real-time. The system's ability to integrate with existing school databases further enhances its practicality and usability
Sentiment Analysis of Indonesian Society Towards the Merdeka Belajar Policy on Twitter Social Media Justam; Muchtar, Ardiansyah AR.; Kurniawan, Fahmi; Erlita; Hijrawati
Bahasa Indonesia Vol 15 No 02 (2023): Instal : Jurnal Komputer Periode (Juli-Desember)
Publisher : Cattleya Darmaya Fortuna

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54209/jurnalinstall.v15i02.341

Abstract

The Ministry of Education and Culture (Kemendikbud) announced the Merdeka Belajar policy in early 2020 as an effort to reform the national education system. This policy has generated various responses from the public, including both support and criticism. Twitter, as one of the most widely used social media platforms, has become a primary medium for the public to express their opinions, feedback, and perspectives on this policy. Sentiment analysis can be utilized to identify and classify public opinions embedded in Twitter posts to understand societal responses toward the Merdeka Belajar policy. This study aims to develop a sentiment analysis model for the Merdeka Belajar policy using a Convolutional Neural Network (CNN) algorithm. The model is designed to classify sentiments into three categories: positive, negative, and neutral. Additionally, this study applies hyperparameter tuning to optimize the model’s performance in sentiment classification. Hyperparameter tuning is conducted to determine the best parameter combination to enhance the model's accuracy. The results indicate that the developed model achieves a sentiment classification accuracy of 82.54%.
Application of Histogram of Oriented Gradients (HOG) for Evaluating Students' Visual Attention in Online Learning via Zoom Justam; Muchta, Ardiansyah AR.; Khalid Ilyas, Irsyad; Erlita; Ramadani, Aisya
Bahasa Indonesia Vol 15 No 02 (2023): Instal : Jurnal Komputer Periode (Juli-Desember)
Publisher : Cattleya Darmaya Fortuna

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54209/jurnalinstall.v15i02.343

Abstract

Online learning is one of the implementations of distance education that continues to develop, particularly with the utilization of video conferencing applications such as Zoom Meeting. This study aims to analyze the effectiveness of using Zoom Meeting in online learning by focusing on detecting students' attention levels based on blink analysis. In this research, an eye detection module was developed using the Dlib Model and Facelandmark, with the Histogram of Oriented Gradients (HOG) method as a feature extraction technique. Blink analysis was conducted to determine the blink ratio, which serves as an indicator of an individual's attention level. Generally, attention levels can be identified through blinking patterns, where fatigue or lack of focus is reflected in higher blink frequency. The study results show that the developed system can identify an individual's focus level with a highest accuracy of 95.56% in tests with three subjects, while the lowest accuracy was 72.24% in tests with 16 subjects. Based on the analysis of blink frequency during learning sessions using Zoom Meeting, it can be concluded that the average student focus level remains within the normal range.
Bibliometric Review on Infrastructure Monitoring with IoT Judijanto, Loso; Justam, Justam; Nampira, Ardi Azhar
West Science Interdisciplinary Studies Vol. 3 No. 06 (2025): West Science Interdisciplinary Studies
Publisher : Westscience Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58812/wsis.v3i06.2013

Abstract

The integration of the Internet of Things (IoT) into infrastructure monitoring has transformed how built environments are observed, maintained, and managed. This study conducts a comprehensive bibliometric review to map the research landscape, thematic trends, and collaboration patterns in the domain of IoT-based infrastructure monitoring. Using data retrieved from the Scopus database (2010–2024) and analyzed through VOSviewer, the study identifies key research clusters, influential authors, prolific countries, and the evolution of core topics over time. Results show that the research focus has shifted from basic sensor deployment and data acquisition to advanced topics such as machine learning, edge computing, data privacy, and cybersecurity. India, China, and the United States emerge as leading contributors, with dense global collaboration networks. The study highlights both the maturity of core research areas and the emergence of new directions such as blockchain integration and privacy-preserving infrastructure systems. These findings provide valuable insights for academics, policymakers, and practitioners aiming to enhance infrastructure resilience and efficiency through IoT technologies.
Bibliometric Review on Infrastructure Monitoring with IoT Judijanto, Loso; Justam, Justam; Nampira, Ardi Azhar
West Science Interdisciplinary Studies Vol. 3 No. 06 (2025): West Science Interdisciplinary Studies
Publisher : Westscience Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58812/wsis.v3i06.2013

Abstract

The integration of the Internet of Things (IoT) into infrastructure monitoring has transformed how built environments are observed, maintained, and managed. This study conducts a comprehensive bibliometric review to map the research landscape, thematic trends, and collaboration patterns in the domain of IoT-based infrastructure monitoring. Using data retrieved from the Scopus database (2010–2024) and analyzed through VOSviewer, the study identifies key research clusters, influential authors, prolific countries, and the evolution of core topics over time. Results show that the research focus has shifted from basic sensor deployment and data acquisition to advanced topics such as machine learning, edge computing, data privacy, and cybersecurity. India, China, and the United States emerge as leading contributors, with dense global collaboration networks. The study highlights both the maturity of core research areas and the emergence of new directions such as blockchain integration and privacy-preserving infrastructure systems. These findings provide valuable insights for academics, policymakers, and practitioners aiming to enhance infrastructure resilience and efficiency through IoT technologies.
Optimasi Kinerja Sensor Ultrasonik pada Prototype Sistem Monitoring Slot Parkir Fitriani, Fitriani; Saenong, Andi; Idris, Mochammad Agus; Justam, Justam
TIN: Terapan Informatika Nusantara Vol 6 No 2 (2025): July 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v6i2.8066

Abstract

The growth of private vehicles in urban areas poses significant challenges in managing parking spaces efficiently and accurately. The Internet of Things (IoT) technology offers a promising solution for developing an automated parking slot monitoring system based on sensors. This study aims to optimize the performance of ultrasonic sensors by evaluating two different installation positions: at the rear (Scenario A) and above (Scenario B) the parking slot. The research was conducted using an experimental approach with a miniature prototype, where data were collected and evaluated based on detection accuracy. The results showed that Scenario B achieved an accuracy of 85%, whereas Scenario A reached only 55%, with a 30% accuracy difference. These findings indicate that sensor placement greatly influences system performance in detecting the presence of vehicles. This study provides a significant contribution to the development of more accurate and efficient IoT-based automated parking systems.
Co-Authors Abdul Malik Andi Saenong Ardiansyah, Muh. Batari Tammaka, Arifah Batti, Sartho Chai, Som Diva, Clara ERLITA Erlita, Erlita Fitri, Nurul Fitriani Fitriani Hasanuddin, M. Hasanuddin Hijrawati Husni Mubarak Idris, Mochammad Agus Jamilah, Nur Judijanto, Loso Khalid Ilyas, Irsyad Kurniawan, Fahmi Lewa, Revah Oktria Luqman Fanani Mz M. Hasanuddin Mangellak, Deo Muchta, Ardiansyah AR. Muchtar, Ardiansyah AR. Nampira, Ardi Azhar Nurjanah, Titin Pong, Ming Rahmah Rahmah Ramadani, Aisya Ramba, Jousadrah Sibiti, Milda Sitti Mawaddah Umar Takbir, Muh. Nashir Yuyun, Yuyun