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All Journal Jutisi: Jurnal Ilmiah Teknik Informatika dan Sistem Informasi bit-Tech Infotek : Jurnal Informatika dan Teknologi Jurnal Sintaks Logika (JSilog) SmartComp Journal of Innovative and Creativity Journal of Education Transportation and Business
Bambang Irawan
Muhadi Setiabudi University
Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media Mathematics Social Sciences Transportation Other

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Pemetaan Stasiun Kereta Api di Kabupaten Brebes Berbasis Web Dwi Angga Fahrezi; Bambang Irawan; Agyztia Premana
Journal of Education Transportation and Business Vol 1, No 2 (2024): Desember 2024
Publisher : CV. Rayyan Dwi Bharata

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57235/jetbus.v1i2.3386

Abstract

Kabupaten brebes terletak di Provinsi Jawa Tengah. Ibu Kota Kabupaten Brebes terdapat di Kecamatan Brebes. Kabupaten brebes terdapat banyak Stasiun Kereta Api sehingga diperlukan platform yang dapat menyajikan Informasi Geografis yang cepat dan akurat. Metode yang digunakan dalam penelitian ini metode Deskriptif menggunakan pendekatan Sistem Informasi Geografis (SIG) untuk memetakan lokasi stasiun kereta api. Hasil dari penelitian ini adalah peta berbasis web yang menampilkan informasi tentang Stasiun Kereta Api. Sistem ini diharapkan dapat memudahkan masyarakat dalam mengakses informasi perkeraapian.
Penerapan Vision Transformer Untuk Klasifkasi Sampah Rumah Tangga Prasista Dhiyaul Haq; Bambang Irawan
Journal of Innovative and Creativity Vol. 6 No. 1 (2026)
Publisher : Fakultas Ilmu Pendidikan Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/joecy.v6i1.7826

Abstract

The increasing volume of Household waste requires an accurate and efficient automatic waste sorting system. This study aims to apply Vision Transformer (ViT) for image-based household waste clasification. The dataset was divided inti training and validation sets and prepared to match the Vision Transformer archtecture. The ViT-Base Patch16-224 model was trained using the AdamW optimizer with a learning rate of 0.0002, batch size of 16, and 15 training epoch. Model performence was evaluated using accuracy, precision, recall, F1-score, and confusion matrix. Experimental results show that the proposed model achieved an overall accuracy of 95%. The inorganic class obtained a precision of 0.9, recall of 0.96, and F1-score of 0.95, while the organic class achived a precision of 0.94, recall of 0.93, F1-score of 0.94. these result indicate that self-attention mechanism in Vision Transformer effectively extracts global visual features and improves clasification stability. Therefore, Vision Transformer dermonstrates strong potential for implementasi in intelligent automatic waste sorting systems.
Classification of Sentiment of Emina Product Reviews Using the Naive Bayes Algorithm Wiwik Astriani; Otong Saeful Bachri; Bambang Irawan
bit-Tech Vol. 8 No. 2 (2025): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i2.3554

Abstract

The rapid development of e-commerce in Indonesia has led to an increase in the number of consumer reviews containing opinions and experiences of using products. In the cosmetic product category, text reviews have an important role in influencing purchasing decisions. However, the large volume of data and the imbalance of sentiment distribution are the main challenges in conducting manual and accurate sentiment analysis. Therefore, an automated approach based on machine learning is needed that is efficient and capable of handling large-scale and unbalanced data. This study aims to analyze the sentiment of reviews of Emina brand cosmetic products on the Tokopedia platform and evaluate the effectiveness of the Multinomial Naïve Bayes algorithm combined with TF-IDF and SMOTE data balancing techniques in classifying positive, neutral, and negative sentiments. The research data was obtained through web scraping of Emina product reviews, resulting in 446,325 review data. The research stages include text preprocessing, rule-based sentiment labeling, feature extraction using TF-IDF, data balancing using SMOTE, and classification modeling with the Naïve Bayes Multinomial algorithm. Model performance evaluation was carried out using accuracy, precision, recall, F1-score, and confusion matrix metrics. The test results showed that the model achieved an accuracy of 94.72% with a stable F1-score value in all sentiment classes, including minority classes, after the implementation of SMOTE. This study proves that the combination of Multinomial Naïve Bayes, TF-IDF, and SMOTE is effective for large-scale analysis of cosmetic product review sentiment and is able to significantly overcome the problem of data imbalance.
Analisis Spasial Jalur Pendakian Gunung Lawu via Cemoro Sewu Berbasis SIG Andin Ayu Oksilia Ramadhani; Nur Ariesanto Ramdhan; Bambang Irawan
Jutisi : Jurnal Ilmiah Teknik Informatika dan Sistem Informasi Vol 15, No 2 (2026): April 2026
Publisher : STMIK Banjarbaru

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35889/jutisi.v15i2.3583

Abstract

This study analyzes the spatial characteristics of the Mount Lawu hiking trail via Cemoro Sewu based on Geographic Information Systems (GIS) by utilizing National Digital Elevation Model (DEMNAS) data. The methods used include the collection of trail and hiking post point data, spatial processing using QGIS, overlay with DEMNAS, as well as elevation profile extraction and slope analysis. The results of the study show that the hiking trail has an elevation range from approximately 1,913 meters to approximately 3,229 meters above sea level with a total elevation gain of around 1,316 meters. Elevation profile analysis shows a gradual increase pattern, with the most significant segment being from Post 1 to Post 2, which has the highest elevation gain. In addition, slope analysis results show that the trail is dominated by moderate to steep slope classes (approximately 23°–30°), especially in the middle sections up to near the summit. The spatial information produced in the form of route maps, elevation profiles, and slope distribution is able to provide a quantitative picture of the difficulty level of the route.Keywords: Geographic information system; Demnas; Hiking trails; Elevation profile; Mount Lawu AbstrakPenelitian ini menganalisis karakteristik spasial jalur pendakian Gunung Lawu via Cemoro Sewu berbasis Sistem Informasi Geografis (SIG) dengan memanfaatkan data Digital Elevation Model Nasional (DEMNAS). Metode yang digunakan meliputi pengumpulan data jalur dan titik pos pendakian, pengolahan spasial menggunakan QGIS, overlay dengan DEMNAS, serta ekstraksi profil elevasi dan analisis kemiringan lereng. Hasil penelitian menunjukkan bahwa jalur pendakian memiliki rentang elevasi dari ±1.913 mdpl hingga ±3.229 mdpl dengan total kenaikan elevasi sekitar ±1.316 meter. Analisis profil elevasi menunjukkan pola kenaikan bertahap dengan segmen paling signifikan berada pada Pos 1–Pos 2 sebagai bagian dengan kenaikan elevasi tertinggi. Selain itu, hasil analisis kemiringan lereng menunjukkan bahwa jalur didominasi oleh kelas lereng sedang hingga curam (±23°–30°), terutama pada bagian tengah hingga mendekati puncak. Informasi spasial yang dihasilkan berupa peta jalur, profil elevasi, dan distribusi kemiringan lereng mampu memberikan gambaran kuantitatif mengenai tingkat kesulitan jalur. 
Analisis Keamanan Pertukaran Data Arsitektur Client-Server Menggunakan Protokol JSON Web Token dan Enkripsi AES Mohammad Romdlon; Bambang Irawan; Nur Ariesanto Ramdhan
Smart Comp :Jurnalnya Orang Pintar Komputer Vol 15, No 3 (2026): Smart Comp: Jurnalnya Orang Pintar Komputer
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/smartcomp.v15i3.10405

Abstract

Pertukaran data pada arsitektur client-server menggunakan protokol HTTP standar sangat rentan terhadap serangan packet sniffing, di mana pihak tidak bertanggung jawab dapat menyadap dan membaca data sensitif secara langsung. Penelitian ini bertujuan untuk meningkatkan keamanan komunikasi data dengan mengimplementasikan metode autentikasi JSON Web Token (JWT) yang dikombinasikan dengan algoritma kriptografi Advanced Encryption Standard (AES). Metode penelitian dilakukan secara eksperimental di laboratorium dengan menguji fungsionalitas aplikasi, menguji ketahanan jaringan dari serangan sniffing menggunakan Wireshark, serta mengukur lonjakan waktu komputasi (overhead response time) menggunakan Postman. Hasil penelitian menunjukkan bahwa sistem berhasil mengamankan jalur komunikasi; data yang disadap melalui jaringan terbukti telah berubah menjadi ciphertext yang tidak dapat dibaca. Selain itu, penambahan lapisan keamanan kriptografi ini terbukti sangat efisien, di mana rata-rata lonjakan waktu respons yang dihasilkan hanya sebesar 4,87 milidetik (dari 9,97 ms menjadi 14,83 ms). Kesimpulannya, kombinasi metode JWT dan enkripsi AES sangat efektif dan direkomendasikan untuk mengamankan pertukaran data pada sistem client-server tanpa mengorbankan performa jaringan secara signifikan.Kata Kunci – Client-Server; Enkripsi AES; JSON Web Token; Keamanan Jaringan; Sniffing 
Optimasi Mobilenetv2 Dengan Transfer Learning Untuk Klasifikasi Penyakit Daun Cabai Rizkon Jajila; Nur Ariesanto Ramdhan; Puji Wahyuningsih; Bambang Irawan
Infotek: Jurnal Informatika dan Teknologi Vol. 9 No. 1 (2026): Infotek : Jurnal Informatika dan Teknologi
Publisher : Fakultas Teknik Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29408/jit.v9i1.33812

Abstract

This research effort seeks to establish a robust classification model for chili leaf diseases through optimization of the MobileNetV2 architecture using transfer learning methodology. The presence of diseases in chili plants is often a major barrier to agricultural productivity, necessitating the development of a rapid and accurate early detection system. The dataset used for this investigation includes six different leaf condition categories, specifically: Bacterial Spot, Cercospora Leaf Spot, Leaf Curl Virus, Healthy Leaf, Nutrient Deficiency, and White Spot. The investigation process begins with an image pre-processing phase and the application of data augmentation techniques, which aim to increase the variability of the training data while simultaneously reducing the risk of overfitting. Next, the model is trained using pre-trained weights from ImageNet, which are adjusted to align with the inherent visual characteristics of chili leaves. Model evaluation is conducted rigorously based on accuracy, precision, recall, and F1-score metrics. The experimental results demonstrate outstanding performance, with the model achieving an accuracy rate of 99%, an average F1-score of 0.99, and a validation loss of 0.07. These figures demonstrate the model's highly competent generalization ability when applied to new data. Analysis facilitated by a confusion matrix found a very low error rate, with only 11 images (0.73%) misclassified out of a total of 1,500 test images. These results support the assertion that MobileNetV2 optimization is highly efficient and accurate in identifying chili leaf diseases. This model has significant potential for integration into mobile devices or digital image-based smart farming systems, thereby assisting farmers in making informed decisions in real time.
Perbandingan Analisis Sentimen Untuk Prediksi Kepuasan Ulasan Produk Kopi Pada Media Sosial Menggunakan Algoritma Svm Dan Naïve Bayes Trisena Pramuja; Bambang Irawan
Jurnal Sintaks Logika Vol. 6 No. 1 (2026): Januari 2026
Publisher : Fakultas Teknik Universitas Muhammadiyah Parepare

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31850/jsilog.v6i1.4284

Abstract

The development of social media has led to a significant increase in the number of consumer reviews of various types of products, including coffee products. To help manufacturers understand consumer satisfaction levels more efficiently, sentiment analysis is a relevant method because it is able to identify opinions automatically. This study compares the performance of two widely used algorithms, namely Support Vector Machine (SVM) and Multinomial Naive Bayes (MNB), in predicting sentiment on consumer reviews related to coffee products on social media. The dataset was analyzed through the stages of text cleanup, TF-IDF transformation, and label encoding process. Both models are developed using a uniform pipeline with consistent parameters to ensure an objective performance comparison. The results show that SVM algorithms with linear kernels produce the highest accuracy compared to Naive Bayes. In addition, a confusion matrix is applied to evaluate the accuracy of predictions in each sentiment category. These findings confirm that SVM is more effective in short-text-based sentiment analysis tasks, such as product reviews on social media platforms.
Co-Authors Agyztia Premana Andin Ayu Oksilia Ramadhani Dwi Angga Fahrezi Mohammad Romdlon Nur Ariesanto Ramdhan Nur Ariesanto Ramdhan Otong Saeful Bachri Prasista Dhiyaul Haq Puji Wahyuningsih Rizkon Jajila Trisena Pramuja Wiwik Astriani