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All Journal JURNAL MEDIA INFORMATIKA BUDIDARMA JOURNAL OF APPLIED INFORMATICS AND COMPUTING JURNAL INSTEK (Informatika Sains dan Teknologi) Jiko (Jurnal Informatika dan komputer) Conference on Innovation and Application of Science and Technology (CIASTECH) Digital Zone: Jurnal Teknologi Informasi dan Komunikasi Jurnal Kreativitas PKM Jurnal Abdimas Mahakam Building of Informatics, Technology and Science Progresif: Jurnal Ilmiah Komputer Systematics Techno Xplore : Jurnal Ilmu Komputer dan Teknologi Informasi Buana Ilmu Jurnal Tekinkom (Teknik Informasi dan Komputer) JOURNAL OF INFORMATION SYSTEM MANAGEMENT (JOISM) JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Jurnal Sistem Komputer dan Informatika (JSON) Jurnal Teknik Informatika (JUTIF) Journal of Applied Data Sciences Jurnal Pendidikan dan Teknologi Indonesia Bulletin of Computer Science Research KLIK: Kajian Ilmiah Informatika dan Komputer Scientific Student Journal for Information, Technology and Science Jurnal Informatika Teknologi dan Sains (Jinteks) Jurnal Buana Pengabdian Eduvest - Journal of Universal Studies Jurnal Pengabdian Masyarakat Nasional Inovasi Gagasan Abdimas & Kuliah Kerja Nyata
Indra, Jamaludin
Universitas Buana Perjuangan Karawang
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DETEKSI PELANGGARAN PENGGUNAAN HELM DENGAN METODE  SSD DAN ARSITEKTUR MOBILENETV2 Purnama, Ariya; Indra, Jamaludin; Arum Puspita Lestari, Santi; Faisal, Sutan
Journal of Information System Management (JOISM) Vol. 7 No. 1 (2025): Juni
Publisher : Universitas Amikom Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24076/joism.2025v7i1.2071

Abstract

Peningkatan jumlah kendaraan terus didominasi oleh pengendara sepeda motor. Terkait hal ini, pengawasan keselamatan lalu lintas oleh pihak berwenang perlu ditingkatkan. Namun, dengan kemajuan teknologi yang pesat, terutama di bidang visi komputer, solusi baru telah dimungkinkan. Salah satu pengembangan tersebut adalah penggunaan perangkat Raspberry Pi yang murah, yang dapat melakukan tugas-tugas yang mirip dengan komputer desktop. Tujuan dari penelitian ini adalah untuk mengembangkan sistem pendeteksi helm untuk pengendara sepeda motor menggunakan arsitektur MobileNetV2 dan SSD (Single Shot Multibox Detector). Sebanyak 1.363 gambar digunakan, dengan 953 untuk pelatihan, 273 untuk validasi, dan 137 untuk pengujian. Tahap prapemrosesan gambar melibatkan pengubahan ukuran gambar menjadi 240 x 240 piksel sebelum dimasukkan ke dalam model. Hasil pelatihan menunjukkan akurasi maksimum sebesar 100%, sementara evaluasi model pada set pengujian mencapai akurasi deteksi 95% untuk pengendara yang mengenakan helm dan 98% untuk mereka yang tidak mengenakan helm. Selain itu, model tersebut mencapai rata-rata Average Precision (mAP) sebesar 99% pada ambang batas IoU 0.5 (mAP50) dan 66% pada ambang batas IoU 0.75 (mAP75).
PREDIKSI RISIKO ANGKA STUNTING PADA BALITA MENGGUNAKAN ALGORITMA SUPPORT VECTOR MACHINE Romlah, Romlah; Faisal, Sutan; Rahmat, Rahmat; Indra, Jamaludin
Jurnal Informatika Teknologi dan Sains (Jinteks) Vol 7 No 2 (2025): EDISI 24
Publisher : Program Studi Informatika Universitas Teknologi Sumbawa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51401/jinteks.v7i2.5749

Abstract

Masalah kekurangan gizi pada balita berdampak serius terhadap pertumbuhan fisik dan perkembangan kognitif anak. Penelitian ini bertujuan untuk memprediksi risiko kondisi tersebut menggunakan algoritma Support Vector Machine (SVM). Data yang digunakan berasal dari Puskesmas Anggadita, Karawang, sebanyak 1.028 data balita. Proses analisis dilakukan melalui pembersihan data, normalisasi, encoding, pembagian data latih dan uji, serta pelatihan model dengan kernel linear. Hasil pengujian menunjukkan bahwa model mampu mengklasifikasikan kategori “tidak mengalami gangguan pertumbuhan” dengan akurasi tinggi, namun belum optimal dalam mengidentifikasi kategori sebaliknya. Akurasi keseluruhan model mencapai 80%. Temuan ini mengindikasikan bahwa SVM dapat digunakan sebagai model awal prediksi, namun perlu perbaikan lebih lanjut dalam penanganan ketidakseimbangan data.
Perbandingan Algoritma Logistic Regression dan K-Nearest Neighbor Dalam Klasifikasi Kematangan Buah Pepaya Wildan Amin Wiharja; Tohirin Al Mudzakir; Hilda Yulia Novita; Jamaludin Indra
Bulletin of Computer Science Research Vol. 5 No. 4 (2025): June 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v5i4.550

Abstract

Visual assessment of papaya ripeness often leads to inconsistent and low accuracy results. To address this, the study applies Logistic Regression and K-Nearest Neighbor (K-NN) algorithms for automatic classification using digital image processing. The initial dataset consisted of 300 images, which were expanded to 1,200 through preprocessing and augmentation. Features were extracted using the Gray Level Co-occurrence Matrix (GLCM) method, and the data was split into 80% for training and 20% for testing. The study aims to compare the performance of both algorithms and understand their classification mechanisms. Results show that K-NN with k=1 achieved an accuracy of 87%, while Logistic Regression with L2 regularization reached 73%, indicating that K-NN outperforms Logistic Regression in classifying papaya ripeness levels.
Sentiment Analysis of User Reviews of the AdaKami Online Loan App from the App Store Using SVM and Naive Bayes Azzahra, Wava Lativa; Jamaludin Indra; Rahmat, Rahmat; Sutan Faisal
Journal of Applied Informatics and Computing Vol. 9 No. 3 (2025): June 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i3.9536

Abstract

This study aims to classify sentiments on user reviews of the AdaKami online loan application, which are obtained through web scraping techniques from the Apple App Store platform. A total of 2000 reviews were collected, then selected and 1000 reviews were selected to be manually labeled by two linguistic experts, to ensure the validity of the classification. Sentiments are divided into three categories, namely negative, neutral, and positive. The classification model was built using two machine learning algorithms, namely Support Vector Machine (SVM) and Naïve Bayes (NB). The evaluation was carried out by measuring accuracy, precision, recall, F1-score, as well as through confusion matrix and cross-validation. The results showed that SVM performed better, with an accuracy of 97.5%, an F1-score of 0.97, and an average cross-validation accuracy of 84.69%. In contrast, Naïve Bayes recorded an accuracy of 81.4% and an F1-score of 0.77. The results of the paired t-test showed that the difference in performance between the two models was statistically significant (p < 0.05). The SVM model was then applied to predict 971 unlabeled reviews, and the results showed a dominance of negative sentiment. Wordcloud visualizations reinforced this finding, with words such as “bilih”, “bunganya”, and “teror” as the most frequently occurring words. These findings prove that SVM is more effective in classifying online loan review sentiments, as well as providing important insights for developers in understanding user perceptions and experiences.
Application of Convolutional Neural Network (CNN) Algorithm with ResNet-101 Architecture for Monkey Pox Detection in Human Al Fathir Rizal Januar; Indra, Jamaludin; Kusumaningrum, Dwi Sulistya; Faisal, Sutan
Journal of Applied Informatics and Computing Vol. 9 No. 3 (2025): June 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i3.9621

Abstract

Monkeypox is a zoonotic disease that has spread to various countries, including Indonesia. It is transmitted through direct contact with skin lesions, respiratory droplets, or contaminated objects. Early and accurate detection is crucial to reduce the risk of transmission and improve treatment effectiveness. This study aims to detect monkeypox using a Convolutional Neural Network (CNN) with the ResNet-101 architecture. The pre-processing steps include normalization and resizing of images to 224×224 pixels. The model is trained using the Adam optimizer, categorical crossentropy loss function, and an adaptive learning rate reduction. Evaluation results show that the model achieved an accuracy of 94%, with a precision of 0.92, recall of 0.92, and an F1-score of 0.92. The model is capable of classifying images effectively, although some misclassifications still occur. This system is intended to function as an initial image-based screening tool, but its results should be confirmed through clinical diagnosis and laboratory testing to ensure accuracy.
Improvement of FPS and Efficiency of Parameters Mask R-CNN with MobileNetV3 Small for Cardboard Detection Tri Vicika, Vikha; Indra, Jamaludin; Faisal, Sutan; Hikmayanti, Hanny
Digital Zone: Jurnal Teknologi Informasi dan Komunikasi Vol. 16 No. 1 (2025): Digital Zone: Jurnal Teknologi Informasi dan Komunikasi
Publisher : Publisher: Fakultas Ilmu Komputer, Institution: Universitas Lancang Kuning

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31849/digitalzone.v16i1.26349

Abstract

Inventory management in warehouses often experiences discrepancies in recording the number of cardboard boxes due to errors during the manual recording process. To overcome this problem, a cardboard detection method was developed using the Default Mask R-CNN model and a modified model using MobileNetV3 Small. The training data was obtained from a collection of cardboard photos which then went through an annotation stage. In the cReonfiguration stage, various anchor scales were applied to determine the bounding box parameters, while the training process used Stochastic Gradient Descent (SGD). The default model is trained with the initial Mask R-CNN settings, while the custom model modifies the backbone and Feature Pyramid Network (FPN) adjustments. The test results show that the custom model has higher efficiency with a parameter count of 20,857,704 and an average FPS of 10.92. However, the accuracy level of the custom model is lower than that of the default model
Klasifikasi Daun Mangga Yang Terkena Hama Dengan Metode Gray Level Co-occurrence Matrix Menggunakan Support Vector Machine Dan K-Nearest Neighbor Berbasis Data Kaggle Nursyawalni, Reva; Indra, Jamaludin; Rohana, Tatang; Wahiddin, Deden
Jurnal Pendidikan dan Teknologi Indonesia Vol 5 No 9 (2025): JPTI - September 2025
Publisher : CV Infinite Corporation

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jpti.1009

Abstract

Penurunan produksi buah mangga di sebabkan oleh kerusakan atau serangan hama pada daun mangga ada beberapa jenis hama pada daun mangga yang umum menyerang antara lain kutu daun (Aphis gossypii), bercak daun alternaria, anthracnose, penggerek batang dan lain-lain. Untuk memperoleh hasil klasifikasi yang lebih akurat dan performa model yang optimal, dibutuhkan sistem yang mampu menghasilkan tingkat akurasi terbaik. Sebagai respons terhadap urgensi tersebut, penelitian ini bertujuan untuk mengklasifikasikan daun mangga yang terkena hama dengan memanfaatkan algoritma Support Vector Machine dan K-Nearest Neighbor, serta penggunaan Gray Level Co-occurrence Matrix sebagai metode untuk mengekstraksi tekstur gambar. Rangkaian tahapan dalam penelitian ini meliputi pre-processing, augmentasi data, ekstraksi fitur, proses klasifikasi oleh kedua algoritma, dan dievaluasi menggunakan akurasi. Hasilnya, algoritma Support Vector Machine  dengan kernel Radial Basis Function mencapai 78% untuk algoritma K-Nearest Neighbor mencapai akurasi 80% dengan ketanggaan k=3
Co-Authors AA Sudharmawan, AA Abdul Gapur Achmad, Syifa Latifah Adi Rizky Pratama Adi Rizky Pratama Agung Susilo Yuda Irawan Ahmad Afifur Rahman Ahmad Fauzi Ahmad Fauzi Ahmad Rahman Al Fathir Rizal Januar Alif Kirana Anton Romadoni Junior Apriade Voutama April Hananto Ardiansyah, Fikri Arif Nurman Arip Solehudin Aris Martin Kobar Arum Puspita Lestari, Santi Asep Jamaludin Aviv Yuniar Rahman Awal, Elsa Elvira Ayu Juwita Ayu Ratna Juwita Azis Saputra Azzahra, Wava Lativa Baihaqi, Kiki Ahmad Cici Emilia Sukmawati Dadang Yusup Deden Wahiddin Deny Maulana Dwi Sulistya Kusumaningrum Dwi Vina Wijaya Eko Pramono Fadmadika, Fadilla Faisal, Sutan Fauzi Ahmad Muda Fauzi, Ahmad Firdaus, Thoriq Janati Firmansyah Maulana Fitri Nur Masruriyah, Anis Garno . Garno, Garno Gugy Guztaman Munzi Hanny Hikmayanti Handayani Hanung Pangestu Rahman Hilda Fitriana Dewi Hilda Novita Hilda Yulia Novita Irma Putri Rahayu Juwita, Ayu Ratna Karyanto, Dony Dwi Khoirull Munazzal Kusumaningrum, Dwi Sulistya Lestari, Santi Arum Puspita M Andrian Agustyan Maharina, Maharina Maliah Andriyani Mudzakir, Tohirin Al Muhammad Cesar Afriansyah Arief Muhammad Deden Miftah Fauzi Muhammad Imam Naufal Muhammad Khoiruddin Harahap Muhammad Raja Nurhusen Muhammad Romadhon Nazori AZ Novalia, Elfina Nugraha, Najmi Cahaya Nurdin, Cherry Januar Nurlaelasari, Euis Nursyawalni, Reva Paryono, Tukino Pratama, Adi Rizky Purnama, Ariya Purnomo, Indarto Aditya Rahmat Hidayat Rahmat Rahmat Rahmat Rahmat Rija Nur Hijriyya Rissa Ilmia Agustin Rizki, Lutfi Trisandi Rizky Rifaldi Robinson Nababan Rohana, Tatang Romlah Saefulloh, Nandang Sandi Susanto Santi Lestari Sihabudin Sihabudin, Sihabudin Siregar, Amril Mutoi Siti Robiah Suparno Sutan Faisal Syahrul Azis Tatang Rohana Tia Astiyah Hasan Tohirin Al Mudzakir Tohirin Mudzakir Toif Muhayat Tri Vicika, Vikha Ulfa Amelia Wahiddin, Deden Wildan Amin Wiharja Yana Cahyana Yogi Firman Alfiansyah