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All Journal Edulib JSI (Jurnal sistem Informasi) Universitas Suryadarma JATI (Jurnal Mahasiswa Teknik Informatika) CIVITAS (JURNAL PEMBELAJARAN DAN ILMU CIVIC) Instal : Jurnal Komputer Journal of Artificial Intelligence and Engineering Applications (JAIEA) AMMA : Jurnal Pengabdian Masyarakat Jurnal Informatika Polinema (JIP) Jurnal Kecerdasan Buatan dan Teknologi Informasi
Pratama, Denni
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Humanities Automotive Engineering Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Nursing Social Sciences

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Analisis Uji Beda Metode Pembelajaran Berbasis Masalah Menggunakan Media "Pohon Kebhinekaan" Terhadap Prestasi Belajar Siswa SMAN 1 Kademangan Pratama, Denni; Baihaqi, M. Iqbal; Triantoro, Miranu
CIVITAS (JURNAL PEMBELAJARAN DAN ILMU CIVIC) Vol 11, No 2 (2025)
Publisher : LPPM Universitas Labuhanbatu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36987/civitas.v11i2.7868

Abstract

This study aims to analyse the differences in learning achievement between students taught using the problem-based learning (PBL) method with the ‘Tree of Diversity’ media and students taught using conventional methods in Pancasila Education at SMAN 1 Kademangan. This study uses a quantitative approach with a pretest-posttest control group quasi-experimental design. The research subjects consisted of two 10th-grade classes: an experimental class that received the PBL method treatment using the ‘Tree of Diversity’ media and a control class that used the conventional method. Data were collected through tests (pretest and posttest) and questionnaires to assess student responses. For the analysis, the T-test technique was used. The results of the study showed that there was a significant difference in learning achievement between the two groups, where students taught using the PBL method with the ‘Tree of Diversity’ media had a higher increase in learning achievement.  This can be seen from the learning achievement difference test result of -28.970, which is significantly lower than the t-table value of 1.693. This means that the use of the PBL method with the ‘Tree of Diversity’ media is more effective in supporting students' learning achievements at the high school level. Additionally, students' responses to the use of the method and media were very positive. This can be seen from the students' answers, which reached 44.24 out of a maximum score of 50. Thus, the results of this study also show that the use of learning media based on diversity values in the PBL method can improve the effectiveness of learning and students' understanding of Pancasila values
Implementasi Algoritma CNN dalam Sistem Absensi Berbasis Pengenalan Wajah Aldiani, Dea; Dwilestari, Gifthera; Susana, Heliyanti; Hamonangan, Ryan; Pratama, Denni
Jurnal Informatika Polinema Vol. 10 No. 2 (2024): Vol 10 No 2 (2024)
Publisher : UPT P2M State Polytechnic of Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33795/jip.v10i2.4852

Abstract

Pengembangan teknologi pengenalan wajah telah menjadi peluang untuk meningkatkan efisiensi sistem absensi. Penelitian ini bertujuan untuk mengimplementasikan algoritma Convolutional Neural Network (CNN) dalam sistem absensi berbasis pengenalan wajah. Keunggulan CNN dalam mengekstraksi fitur kompleks dari gambar menjadikannya pilihan yang potensial untuk meningkatkan akurasi pengenalan wajah dalam pengelolaan absensi. Penelitian ini menggunakan kumpulan dataset wajah yang beragam, mencakup variasi sudut pandang, ekspresi, dan kondisi pencahayaan. Data yang digunakan terdiri dari 20 kelas yang masing-masing memiliki 500 data wajah. Penerapan model CNN dimulai dengan perancangan arsitektur CNN sederhana dengan menambahkan lapisan konvolusi, pooling dan fully connected. Model CNN kemudian dilatih menggunakan data latih sebesar 85% dari keseluruhan data. Setelah model dilatih, selanjutnya dilakukan evaluasi model CNN melalui beberapa metrik evaluasi. Dari hasil evaluasi diperoleh tingkat akurasi yang baik sebesar 91%. Setelah memperoleh model CNN untuk pengenalan wajah, model CNN diimplementasikan dalam sistem absensi. Berdasarkan hasil implementasi algoritma CNN terhadap sistem absensi diperoleh proses absensi yang akurat dan efisien sehingga dapat mengatasi kecurangan dan manipulasi data serta meningkatkan efisiensi dalam manajemen kehadiran di berbagai lingkungan.
Analisis Komparatif Multinomial Naïve Bayes dan Logistic Regression untuk Klasifikasi Sentimen Ulasan Pengguna Aplikasi TIX ID Rachmatullah, Mochamad Miftah; Irawan, Bambang; Faqih, Ahmad; Pratama, Denni; Kurnia, Dian Ade
JSI (Jurnal Sistem Informasi) Universitas Suryadarma Vol. 13 No. 1 (2026): JSI (Jurnal sistem Informasi) Universitas Suryadarma
Publisher : Fakultas Ilmu Komputer dan Desain - Unsurya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35968/jsi.v13i1.1723

Abstract

Penelitian ini bertujuan untuk membandingkan performa algoritma Multinomial Naïve Bayes (MNB) dan Logistic Regression (LR) dalam klasifikasi sentimen multi-kelas pada ulasan pengguna aplikasi TIX ID. Sebanyak 2.500 ulasan dikumpulkan melalui proses scraping dari Google Play Store dan diproses melalui tahapan preprocessing, yang meliputi pembersihan teks, case folding, tokenisasi, stopword removal, dan stemming. Dua teknik ekstraksi fitur digunakan, yaitu CountVectorizer dan TF-IDF, sebelum model dilatih menggunakan kedua algoritma. Proses hyperparameter tuning dilakukan menggunakan GridSearchCV dengan lima lipatan cross-validation untuk memperoleh konfigurasi parameter terbaik. Hasil penelitian menunjukkan bahwa MNB dengan CountVectorizer pada tahap sebelum tuning memberikan performa paling unggul, dengan akurasi mencapai 84,80% dan F1-score macro tertinggi dibandingkan kombinasi lainnya. Sementara tuning meningkatkan stabilitas performa model, nilai akurasi tidak melampaui model awal tersebut. Temuan ini menunjukkan bahwa kombinasi MNB dan CountVectorizer lebih sesuai untuk karakteristik teks ulasan aplikasi berbahasa Indonesia yang bersifat sparse dan memiliki pola repetitif. Model terbaik kemudian diimplementasikan dalam sistem analisis sentimen berbasis web yang mampu memproses ulasan secara real time. Penelitian ini memberikan kontribusi pada pengembangan metode analisis sentimen di Indonesia dan penerapannya pada aplikasi layanan digital.
Optimization of Convolutional Neural Networks Using Resizing Techniques for Banana Leaf Disease Classification Kurniawan, Aldiyansyah; Purnamasari, Ade Irma; Pratama, Denni; Tohidi, Edi; Wahyudin, Edi
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 5 No. 2 (2026): February 2026
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v5i2.1876

Abstract

Early and accurate identification of banana leaf diseases is essential for supporting digital agriculture, as visual symptoms often require rapid and reliable analysis. This study investigates the impact of three image resizing techniques squashing, letterboxing, and random resized crop on the performance of the MobileNetV2 architecture in classifying four categories of banana leaf images using the Banana Leaf Disease Dataset v4 consisting of 4,675 samples. The experiments were conducted using a transfer learning approach with an 80:10:10 data split, standardized normalization, and data augmentation. The results show that all resizing techniques achieved test accuracies above 92%. Squashing produced the highest accuracy and fastest training time, letterboxing demonstrated the most stable performance with the lowest validation loss, and random resized crop improved generalization to variations in object position. These findings confirm that resizing strategies significantly influence the stability and effectiveness of CNN models. Overall, MobileNetV2 proves capable of delivering accurate and efficient classification of banana leaf diseases when supported by an appropriate preprocessing pipeline. This study provides empirical evidence for developing image-based plant disease diagnosis systems within smart agriculture.
Application of Weighted Loss Function in Convolutional Neural Network for Acne Image Classification Abubakar Sidik; Purnamasari, Ade Irma; Pratama, Denni; Marta, Puji Pramudya; Wijaya, Yudhistira Arie
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 5 No. 2 (2026): February 2026
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v5i2.1885

Abstract

Automated acne image classification using Convolutional Neural Networks (CNN) holds significant potential in dermatological diagnosis but faces a fundamental challenge of class imbalance. This phenomenon causes standard models to be biased towards majority classes and fail to recognize clinically important minority classes. This study aims to address this bias by applying a Weighted Loss Function to the EfficientNetB1 architecture. The research method employs a comparative experimental approach between two scenarios: the Baseline model (Standard Cross-Entropy) and the Proposed model (Weighted Cross-Entropy). The dataset consists of 5 acne classes with an imbalanced distribution. The results show that the Weighted Loss model significantly outperforms the Baseline model. Overall accuracy increased from 80% to 86%. The most significant improvement occurred in the minority class 'Papules', where the F1-Score surged by 0.10 points (from 0.71 to 0.81). It is concluded that the application of Weighted Loss Function effectively overcomes bias due to imbalanced data without the need for synthetic data augmentation, resulting in a fairer and more reliable model for clinical implementation.
Analysis of the Effectiveness of Manual Deployment and CI/CD Github Actions in the Braisee Application Seputra, Nenda Alfadil; Nurdiawan, Odi; Dikananda, Arif Rinaldi; Pratama, Denni; Kurnia, Dian Ade
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 5 No. 2 (2026): February 2026
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v5i2.1916

Abstract

In the modern cloud-based software development ecosystem, the speed and reliability of the deployment process are critical elements. This study aims to evaluate the effectiveness of implementing Continuous Integration/Continuous Deployment (CI/CD) using GitHub Actions compared to manual methods for the machine learning API of the Braisee application hosted on Google Cloud Run. Using a quantitative approach with a comparative experimental design across ten testing iterations, this research measures deployment time efficiency, error rates, and system stability. The experimental results show a significant performance disparity, where the automated method based on GitHub Actions is considerably more efficient, with an average total duration of 111–167 seconds, reducing operational time by 40–60% compared to the manual method, which requires 297–364 seconds. In terms of reliability, the automated method achieves a 100% success rate with high consistency, whereas the manual method demonstrates substantial vulnerability to human errors such as mistyped project IDs and inconsistent image tagging. It is concluded that implementing CI/CD through GitHub Actions is a superior solution that improves time efficiency and ensures the stability of cloud-based applications compared to manual procedures.
Comparison of TF-IDF and Word2Vec Feature Representations for Emotion Classification of Tokopedia E-Commerce Review Using LinearSVC Azzahra, Fitriyani; Irawan, Bambang; Faqih, Ahmad; Pratama, Denni; Kurnia, Dian Ade
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 5 No. 2 (2026): February 2026
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v5i2.2215

Abstract

This study aims to compare the performance of TF-IDF and Word2Vec feature representations for emotion classification of Tokopedia e-commerce reviews using the LinearSVC algorithm. The dataset used is PRDECT-ID, which consists of 5,400 Indonesian-language reviews labeled with positive and negative emotions. The preprocessing stages include case folding, non-alphabet character cleaning, slang normalization, stopword removal, Sastrawi stemming, and emoji handling. Feature extraction was performed using TF-IDF and Word2Vec, after which the models were trained using LinearSVC and evaluated through 5-Fold Cross Validation and holdout testing. The experimental results show that TF-IDF achieves better performance, with an accuracy of 0.65, a macro-F1 score of 0.645, and a Cohen’s Kappa value of 0.294. Meanwhile, Word2Vec attains an accuracy of 0.58 and a macro-F1 score of 0.540. These findings indicate that TF-IDF is more effective for short and informal texts characteristic of Indonesian e-commerce reviews.
Co-Authors Abubakar Sidik Ade Rizki Rinaldi Adianto, Luthfi Ahmad Faqih Aldiani, Dea Amida, Anggi Fitria Andriyanti, Rina Ardhani, Risma Azhar Azhar Azzahra, Fitriyani Baihaqi, M. Iqbal Bambang Irawan Bambang Siswoyo Bayu Saputra Christina Juliane, Christina Dadang Sudrajat Dikananda, Arif Rinaldi Dikananda, Fatihanursari Dwi Kusuma, Lukman Fatihanursari, Fatihanursari Faturrohman, Faturrohman Gifthera Dwilestari Hamonangan, Ryan Hermawan, Sari Hery Widijanto Hoerunnisa, Anis Iffah Adella, Luthfiyyah Irfan Ali, Irfan Kaslani Kurnia, Dian Ade Kurniawan, Aldiyansyah Marta, Puji Pramudya Maulana, Arman Mubarok, Fatkhan Muhamad Basysyar , Fadhil Ningrum, Cistia Nurhakim, Bani Odi Nurdiawan Permana, Sandy Eka Puri, Anita Purnamasari, Ade Irma Rachmatullah, Mochamad Miftah Rifa’I, Ahmad Rinaldi Dikananda, Arif Rizki Rinaldi , Ade Rohmat, Cep Lukman Saeful Anwar, Saeful Sagita, Ayu Sazwati, Anggi Septiani, Intan Seputra, Nenda Alfadil Siti Nurhasanah Solihudin, Dodi Suarna, Nana Sunana, Heliyanti Susana, Heliyanti Talia, Agita Hany Tohidi, Edi Triantoro, Miranu Veithzal Rivai Zainal Vibrianti, Vera Wahyudin, Edi Yudhistira Arie Wijaya Zamil Farhan, Muhammad Zapar, Rizky