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All Journal Journal of Information Systems and Informatics JATI (Jurnal Mahasiswa Teknik Informatika) Jurnal Infortech Journal of Computer Networks, Architecture and High Performance Computing Jurnal Responsif : Riset Sains dan Informatika Devotion: Journal of Research and Community Service Malcom: Indonesian Journal of Machine Learning and Computer Science Innovative: Journal Of Social Science Research
Hafizh Cahaya Putra, Vito
Universitas Satu
Humanities Computer Science & IT Decision Sciences, Operations Research & Management Education Law, Crime, Criminology & Criminal Justice Library & Information Science Public Health Other

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Early Warning System and Monitoring of River Water Quality Based on Internet of Things Hafizh Cahaya Putra, Vito; Hendayun, Mokhamad; Yustianto , Purnomo
Devotion : Journal of Community Service Vol. 3 No. 1 (2021): Devotion : Journal of Community Service
Publisher : Green Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36418/dev.v3i1.89

Abstract

River conditions in Bandung City are currently in critical condition. This study aims to create an early warning system and monitoring of river water quality based on the Internet of Things in the hope that early warnings sent through the telegram application belonging to the Bandung City DLHK officer and the Twitter social media website, can inform the Bandung City DLHK officer that a river is in a polluted condition and the officer can immediately go to the location of river water to carry out mitigation, and give warnings to the community. The research method used using the waterfall method which consists of: needs analysis, system design, implementation, testing, and maintenance with sequential implementation. Data collection methods were carried out in several ways, namely: interviews, giving questionnaires, and literature studies used in this study sourced from books, journals, seminar presentations, and the internet as references in the research conducted. Based on the research that has been carried out, the following test results are obtained: black box testing is carried out in accordance with those contained in the test plan with the results of each test having valid results. The results obtained from the user acceptance test which are calculated using the Likert scale have an average value of 86.94% which fall into the category of strongly agree, and there are three guidelines which are a follow-up to the output of the early warning system that can be carried out either by the Environmental Service. and Cleanliness (DLHK) of Bandung City and the community.
Early Warning System and Monitoring of River Water Quality Based on Internet of Things Hafizh Cahaya Putra, Vito; Hendayun, Mokhamad; Yustianto , Purnomo
Devotion : Journal of Community Service Vol. 3 No. 1 (2021): Devotion : Journal of Community Service
Publisher : Green Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36418/dev.v3i1.89

Abstract

River conditions in Bandung City are currently in critical condition. This study aims to create an early warning system and monitoring of river water quality based on the Internet of Things in the hope that early warnings sent through the telegram application belonging to the Bandung City DLHK officer and the Twitter social media website, can inform the Bandung City DLHK officer that a river is in a polluted condition and the officer can immediately go to the location of river water to carry out mitigation, and give warnings to the community. The research method used using the waterfall method which consists of: needs analysis, system design, implementation, testing, and maintenance with sequential implementation. Data collection methods were carried out in several ways, namely: interviews, giving questionnaires, and literature studies used in this study sourced from books, journals, seminar presentations, and the internet as references in the research conducted. Based on the research that has been carried out, the following test results are obtained: black box testing is carried out in accordance with those contained in the test plan with the results of each test having valid results. The results obtained from the user acceptance test which are calculated using the Likert scale have an average value of 86.94% which fall into the category of strongly agree, and there are three guidelines which are a follow-up to the output of the early warning system that can be carried out either by the Environmental Service. and Cleanliness (DLHK) of Bandung City and the community.
Penggunaan Computer Vision untuk Menghitung Jumlah Kendaraan dengan Menggunakan Metode SSD (Single Shoot Detector) Tori Sutisna; Agung Rachmat Raharja; Solihin Solihin; Eko Hariyadi; Vito Hafizh Cahaya Putra
Innovative: Journal Of Social Science Research Vol. 4 No. 2 (2024): Innovative: Journal Of Social Science Research
Publisher : Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/innovative.v4i2.10071

Abstract

Pengguna kendaraan di Indonesia semakin meningkat setiap tahunnya, hal ini menjadikan setiap ruas jalan raya macet karena tidak adanya sistem cerdas yang menghitung jumah kendaraan, baik yang masuk ataupun keluar jalan. Dengan adanya sistem cerdas ini memudahkan untuk menghitung kendaraan yang lewat dan dengan adanya sistem ini memudahkan para petugas di jalan raya untuk memantau kendaraan dan dapat di hitung sampai dengan ambang batas kendaraan sesuai dengan yang sudah melewati salah satu jalan. Hal ini memudahkan petugas di jalan raya untuk mengatur lalu lintas atau bisa mengurai kendaraan, sehingga pada satu titik jalan tersebut tidak akan macet dan bisa memakan waktu hingga lama.
SISTEM PERINGATAN DINI BANJIR BANDANG DI WILAYAH PENAMBANGAN PASIR VULKANIK MENGGUNAKAN INTERNET OF THINGS Putra, Vito Hafizh Cahaya; Kanugrahan, Ghanim; Wahyu, Ari Purno
Jurnal Responsif : Riset Sains dan Informatika Vol 6 No 1 (2024): Jurnal Responsif : Riset Sains dan Informatika
Publisher : LPPM Universitas Adhirajasa Reswara Sanjaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51977/jti.v6i1.1498

Abstract

Banjir bandang menjadi ancaman serius yang mengintai bagi siapapun yang beraktivitas disungai, seperti aktivitas penambang pasir vulkanik yang kerap kali mendatangkan korban yaitu secara mendadak banjir bandang datang secara tiba-tiba dari dataran tinggi. Banyak penelitian untuk membuat sistem peringatan dini berbasis Internet of Things (IoT) yang telah dilakukan umumnya menggunakan Water Flow Sensor yang kurang tepat untuk banjir bandang, tidak menggunakan platform Thingspeak dan membutuhkan pembangkit tenaga listrik konvensional. Penelitian ini bertujuan untuk membuat prototipe sistem peringatan dini banjir bandang bagi penambang pasir vulkanik berbasis Internet of Things menggunakan alarm dan media sosial twitter, serta memanfaatkan sensor Ultrasonic, sensor IR (Infrared) Line Tracking, dan modul jaringan ESP8266 menggunakan protokol HTTP (Hypertext Transfer Protocol) untuk terhubung ke platform Thingspeak. Terdapat Solar Cell yang digunakan untuk mengisi daya berbagai perangkat menggunakan cahaya matahari. Setelah dilakukan pengujian pada sistem ini menggunakan Black Box Testing dengan hasil semua perangkat dapat bekerja dengan baik. Peringatan dini melalui alarm terbukti dapat memberikan peringatan secara cepat, dan informasi bencana dapat diakses melalui twitter, serta hasil pendeteksian sensor ditampilkan melalui grafik.
Analisis Sentimen Aplikasi Gojek Menggunakan SVM, Random Forest dan Decision Tree Kanugrahan, Ghanim; Putra, Vito Hafizh Cahaya; Ramdhani, Yudi
Jurnal Infortech Vol 6, No 2 (2024): Desember 2024
Publisher : Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/infortech.v6i2.24594

Abstract

Semakin banyak orang di dunia menggunakan aplikasi seluler di smartphone yang mereka miliki lebih dari sekadar alat hiburan, tetapi juga untuk memenuhi kebutuhan sehari-hari. Hal ini telah menyebabkan munculnya aplikasi seperti Gojek, sebuah perusahaan Super-app yang menyediakan solusi transportasi dan keperluan lainnya. Namun, Gojek menghadapi persaingan dari aplikasi serupa. Dengan kompetisi yang intens, memastikan kepuasan pengguna sangat penting untuk kesuksesan aplikasi Gojek. Review di platform seperti Google Play Store memberikan data berharga bagi pengembang untuk meningkatkan kualitas aplikasi dan pengalaman pengguna melalui pembaruan yang berkelanjutan. Makalah ini menganalisis kepuasan pelanggan aplikasi Gojek menggunakan pembelajaran mesin pada review pengguna dari Google Play Store yang diperoleh dari repositori data Kaggle. Dari 224.044 review awal, dataset dikurangi menjadi 65.584 review. Analisis mengungkapkan sentimen yang bervariasi, dengan kepuasan tinggi pada review bintang 5 dan keluhan umum tentang layanan yang lambat pada penilaian yang lebih rendah. Sembilan variasi model pembelajaran mesin, termasuk SVM, Random Forest, dan Decision Tree, digunakan untuk mengevaluasi data yang diterima. Algoritma SVM diidentifikasi sebagai yang paling efektif untuk klasifikasi sentimen. Hasil ini menunjukkan bahwa algoritma SVM adalah algoritma terbaik untuk digunakan dalam menganalisis review Gojek.
Perancangan Sistem Monitoring Cerdas Berbasis Internet of Things (IoT) dengan Algoritma Random Forest Regression untuk Deteksi Ketinggian pada Tanaman Tomat Cherry: Design of an Intelligent Monitoring System Based on Internet of Things (IoT) with Random Forest Regression Algorithm for Height Detection in Cherry Tomato Plants Putra, Vito Hafizh Cahaya; Al-Husaini, Muhammad; Wahyu, Ari Purno; Raharja, Agung Rachmat
MALCOM: Indonesian Journal of Machine Learning and Computer Science Vol. 5 No. 1 (2025): MALCOM January 2025
Publisher : Institut Riset dan Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/malcom.v5i1.1612

Abstract

Tomat cherry merupakan komoditas bernilai di Indonesia dengan permintaan yang meningkat setiap tahunnya. Penelitian ini mengembangkan sistem pemantauan cerdas berbasis Internet of Things (IoT) untuk tanaman tomat cherry menggunakan algoritma Random Forest Regression (RFR). Sistem ini memanfaatkan mikrokontroler ESP32 dan lima sensor untuk memantau parameter lingkungan, serta aktuator untuk pengaturan kondisi optimal. Data sensor diproses dan disimpan di platform Thingspeak dan diintegrasikan dengan Google Colab untuk prediksi ketinggian tanaman. Hasil prediksi ditampilkan di layar LCD dan dikirimkan sebagai notifikasi melalui aplikasi Telegram. Penelitian ini mengisi kesenjangan dari studi sebelumnya dengan mengintegrasikan berbagai sensor, aktuator, dan platform cloud dalam satu sistem yang komprehensif. Evaluasi sistem menunjukkan nilai Mean Squared Error (MSE) sebesar 0.8294 dan R^2 Score sebesar 0.8939, serta hasil pengujian Black Box Testing memastikan fungsionalitas optimal dalam berbagai skenario. Hasil penelitian ini dapat memberikan manfaat dalam penerapan teknologi IoT dan machine learning untuk monitoring dan pengelolaan tanaman tomat cherry, harapannya meningkatkan efisiensi dan produktivitas pertanian.   
Enhanced Plant Disease Detection Using Computer Vision YOLOv11: Pre-Trained Neural Network Model Application Al Husaini, Muhammad; Rachmat Raharja , Agung; Cahaya Putra , Vito Hafizh; Lukmana, Hen Hen
Journal of Computer Networks, Architecture and High Performance Computing Vol. 7 No. 1 (2025): Article Research January 2025
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v7i1.5146

Abstract

This study investigates the application of YOLOv11, a cutting-edge deep learning model, to enhance the detection of plant diseases. Leveraging a comprehensive dataset of 737 images depicting tomato leaves affected by various diseases, YOLOv11 was trained and evaluated on key performance metrics such as precision, recall, and mAP. Experimental results the model was trained and evaluated on key metrics including accuracy (75.6%), precision (0.80), recall (0.77), and mAP@0.5 (75.6%). Experimental through base architectural such as enhanced feature extraction with C2 modules, improved multi-scale detection using SPPF layers, and optimized non-maximum suppression techniques. These improvements enable the model to achieve stable precision and recall for each class, even in challenging scenarios with overlapping objects and diverse environmental conditions. By addressing practical usability challenges, this system offers a scalable, accessible, and impactful solution for precision agriculture, paving the way for sustainable with this pretrained model. This study underscores the potential of deep learning-based models, particularly YOLOv11, in transforming the way monitoring and disease management are approached, demonstrating its ability to stable accuracy and operational efficiency in real-world applications. Furthermore, the practical usability of the YOLOv11-based system addresses challenges in the domain of precision plant detection desease. By providing a scalable, accessible, and highly efficient solution, the model offering a significant advancement toward sustainable agricultural practices.
PREDIKSI HARGA SAHAM BBCA MENGGUNAKAN METODE LONG SHORT-TERM MEMORY DAN GATED RECURRENT UNIT Hariyanti, Ifani; Putra, Vito Hafizh Cahaya; Raharja, Agung Rachmat
Jurnal Responsif : Riset Sains dan Informatika Vol 7 No 1 (2025): Jurnal Responsif : Riset Sains dan Informatika
Publisher : LPPM Universitas Adhirajasa Reswara Sanjaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51977/jti.v7i1.1901

Abstract

Penelitian ini bertujuan untuk memprediksi harga saham PT Bank Central Asia Tbk (BBCA) menggunakan model Long Short-Term Memory (LSTM) dan Gated Recurrent Unit (GRU). Data harga saham diambil dari Yahoo Finance (2010–2023) sebanyak 3.464 data, mencakup atribut Tanggal, Open, High, Low, Close, Adj Close, dan Volume. Data diproses menggunakan MinMax Scaler sebelum pelatihan model. Model dievaluasi menggunakan MAE, RMSE, dan MAPE untuk mengukur performa prediksi. Hasil penelitian menunjukkan bahwa GRU lebih unggul dibandingkan LSTM dalam memprediksi harga saham BBCA, dengan akurasi prediksi yang lebih mendekati nilai aktual. Dari hasil eksperimen pelatihan model menggunakan dataset harga saham BBCA harian dengan berbagai kombinasi hyperparameter yang ditetapkan, ditemukan bahwa model dengan metrik evaluasi terendah adalah model LSTM dengan batch size 64 dan epoch 20. Model ini memberikan nilai MAE sebesar 158.508342, RMSE sebesar 208.687816, dan MAPE sebesar 2.248164%. Temuan ini diharapkan berkontribusi pada pengembangan analisis keuangan di Indonesia.
STUDI KOMPARATIF ALGORITMA MACHINE LEARNING PADA ANALISIS SENTIMEN MEDIA SOSIAL Panjaitan, Febriyanti; Ce, Win; Oktafiandy, Hery; Kanugrahan, Ghanim; Ramdhani, Yudi; Hafizh Cahaya Putra, Vito; Permai, Antika
JATI (Jurnal Mahasiswa Teknik Informatika) Vol. 9 No. 2 (2025): JATI Vol. 9 No. 2
Publisher : Institut Teknologi Nasional Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36040/jati.v9i2.13277

Abstract

Analisis sentimen di Twitter telah menjadi salah satu topik utama dalam penelitian terkait opini publik di bidang ekonomi, politik, dan isu sosial. Penggunaan machine learning dalam analisis sentimen memungkinkan untuk memproses data teks secara efisien. Penelitian ini bertujuan untuk mengeksplorasi literatur terkait analisis sentimen menggunakan metode machine learning pada Twitter dalam konteks ekonomi, politik, dan isu sosial. Metode yang digunakan adalah Systematic Literature Review (SLR), dengan pengumpulan artikel dari tiga database utama: IEEE Xplore, Google Scholar, dan Scopus. Setelah menerapkan kriteria inklusi dan eksklusi, 45 artikel relevan terpilih untuk dianalisis. Hasil penelitian menunjukkan bahwa Support Vector Machine (SVM) memiliki performa terbaik dengan akurasi rata-rata 85.3%, diikuti oleh Random Forest (83.7%) dan Naïve Bayes (81.5%). KNN dan Decision Tree menunjukkan performa lebih rendah, kemungkinan karena sensitivitas terhadap data yang tidak seimbang. Tren penelitian mengindikasikan bahwa analisis sentimen di bidang ekonomi lebih banyak berkaitan dengan dampak kebijakan ekonomi, di bidang politik fokus pada opini publik terkait pemilu dan kebijakan pemerintah, sementara di bidang isu sosial berkaitan dengan gerakan sosial dan kebijakan kesehatan.
Evaluation of Machine Learning Models for Sentiment Analysis in the South Sumatra Governor Election Using Data Balancing Techniques Panjaitan, Febriyanti; Ce, Win; Oktafiandi, Hery; Kanugrahan, Ghanim; Ramdhani, Yudi; Putra, Vito Hafizh Cahaya
Journal of Information System and Informatics Vol 7 No 1 (2025): March
Publisher : Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v7i1.1019

Abstract

Sentiment analysis is crucial for understanding public opinion, especially in political contexts like the 2024 South Sumatra gubernatorial election. Social media platforms such as Twitter and YouTube provide key sources of public sentiment, which can be analyzed using machine learning to classify opinions as positive, neutral, or negative. However, challenges such as data imbalance and selecting the right model to improve classification accuracy remain significant. This study compares five machine learning algorithms (SVM, Naïve Bayes, KNN, Decision Tree, and Random Forest) and examines the impact of data balancing on their performance. Data was collected via Twitter crawling (140 entries) and YouTube scraping (384 entries), and text features were extracted using CountVectorizer. The models were then evaluated on imbalanced and balanced datasets using accuracy, precision, recall, and F1-score. The Decision Tree and Random Forest models achieved the highest accuracies of 79.22% and 75.32% on imbalanced data, respectively. However, they also exhibited overfitting, as indicated by their near-perfect training performance. Naïve Bayes, on the other hand, demonstrated the lowest accuracy at 54.55% despite achieving high precision, suggesting frequent misclassification, particularly for the minority class. SVM and KNN also struggled with imbalanced data, recording accuracies of 58.44% and 63.64%, respectively. Significant improvements were observed after applying data balancing techniques. The accuracy of SVM increased to 71.43%, and KNN improved to 66.23%, indicating that these models are more stable and effective when class distributions are even. These findings highlight the substantial impact of data balancing on model performance, particularly for methods sensitive to class distribution. While tree-based models achieved high accuracy on imbalanced data, their tendency to overfit underscores the importance of balancing techniques to enhance model generalization.
Co-Authors Agung Rachmat Raharja Al Husaini, Muhammad Al-Husaini, Muhammad Ari Purno Wahyu Wibowo Ce, Win Eko Hariyadi Febriyanti Panjaitan Hariyanti, Ifani Hery Oktafiandi Kanugrahan, Ghanim Lukmana, Hen Hen Mokhamad Hendayun Oktafiandy, Hery Permai, Antika Solihin Solihin Tori Sutisna Yudi Ramdhani Yustianto , Purnomo