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All Journal Jurnal Buana Informatika JURNAL ELEKTRO Scientific Journal of Informatics Register: Jurnal Ilmiah Teknologi Sistem Informasi Jurnal Pemberdayaan Masyarakat Madani (JPMM) INOVTEK Polbeng - Seri Informatika MITRA: Jurnal Pemberdayaan Masyarakat Indonesian Journal of Computing and Modeling JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Jurnal Mantik Jurnal Pelayanan dan Pengabdian Masyarakat (Pamas) Journal of Information Systems and Informatics Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi) Jurnal Mnemonic Jurnal Tekinkom (Teknik Informasi dan Komputer) JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Computer Science and Information Technologies Jurnal Pengabdian Masyarakat Asia Jurnal Restikom : Riset Teknik Informatika dan Komputer International Journal Software Engineering and Computer Science (IJSECS) Jurnal Nasional Teknik Elektro dan Teknologi Informasi Jurnal INFOTEL Jurnal Pendidikan Teknologi Informasi (JUKANTI) Jurnal Indonesia : Manajemen Informatika dan Komunikasi Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen) International Journal of Information Technology and Business INOVTEK Polbeng - Seri Informatika JuTISI (Jurnal Teknik Informatika dan Sistem Informasi)
Christine Dewi
Universitas Kristen Satya Wacana
Agriculture, Biological Sciences & Forestry Arts Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Languange, Linguistic, Communication & Media Library & Information Science Mathematics Public Health Social Sciences Other

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Integrating Real-Time Weather Forecasts Data Using OpenWeatherMap and Twitter Dewi, Christine; Chen, Rung-Ching
International Journal of Information Technology and Business Vol. 1 No. 2 (2019): April: International Journal of Information Techonology and Business
Publisher : Universitas Kristen Satya Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Weather forecasts are made by collecting as much data as possible about the current state of the atmosphere (particularly the temperature, humidity, and wind) and using an understanding of atmospheric processes (through meteorology) to determine how the atmosphere evolves in the future. There are several reasons why weather forecasts are important. It forewarns the people about future weather conditions so that people can plan their activities accordingly. It warns people about the impending severe weather conditions and other weather hazards such as thunderstorms, hurricanes, and heavy rainfalls. Thus far, accurate weather predictions have been able to save the lives of many. At its core, Twitter is a real-time public broadcast channel. These characteristics make Twitter a natural platform for public safety communication and early-warning systems. Furthermore, Twitter became an essential source for up-to-date meteorological data and agency announcements. OpenWeatherMap processes all data in a way that it attempts to provide accurate online weather forecast data and weather maps, such as those for clouds and preciptations Besides, we will use Phyton programming language to get real-time weather data from OpenWeatherMap and post the information to our social media Twitter. Finally, OAuth and Tweepy are a very powerful library that enables the Python code to communicate with Twitter. Tweets about the weather could prove useful to anybody wanting to use it.
A Systematic Review of Deep Learning for Intelligent Transportation Systems with Analysis and Perspectives Hendrawan, Aria; Gernowo, Rahmat; Nurhayati, Oky Dwi; Dewi, Christine
JURNAL INFOTEL Vol 16 No 2 (2024): May 2024
Publisher : LPPM INSTITUT TEKNOLOGI TELKOM PURWOKERTO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/infotel.v16i2.1085

Abstract

This study presents a systematic review of deep learning for intelligent transportation systems. Statistics are used to find the most cited articles, and the number of articles and quotes are used to find the most productive and influential authors, institutions, and countries or regions. Key topics and patterns of change are discovered using the authors’ keywords, and the most common issues and themes are revealed using flow maps and showing the corresponding trends. A co-occurrence keyword network is also developed to present the research landscape and hotspots in the field. The results explain how publications have changed over the past seven years. Researchers can use this study to have a deeper understanding of the current state and future trends in the role of deep learning in intelligent transportation systems.
YOLOv8 Analysis for Vehicle Classification Under Various Image Conditions Panja, Eben; Hendry, Hendry; Dewi, Christine
Scientific Journal of Informatics Vol 11, No 1 (2024): February 2024
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v11i1.49038

Abstract

Purpose: The purpose of this research is to detect vehicle types in various image conditions using YOLOv8n, YOLOv8s, and YOLOv8m with augmentation.Methods: This research utilizes the YOLOv8 method on the DAWN dataset. The method involves using pre-trained Convolutional Neural Networks (CNN) to process the images and output the bounding boxes and classes of the detected objects. Additionally, data augmentation applied to improve the model's ability to recognize vehicles from different directions and viewpoints.Result: The mAP values for the test results are as follows: Without data augmentation, YOLOv8n achieved approximately 58%, YOLOv8s scored around 68.5%, and YOLOv8m achieved roughly 68.9%. However, after applying horizontal flip data augmentation, YOLOv8n's mAP increased to about 60.9%, YOLOv8s improved to about 62%, and YOLOv8m excelled with a mAP of about 71.2%. Using horizontal flip data augmentation improves the performance of all three YOLOv8 models. The YOLOv8m model achieves the highest mAP value of 71.2%, indicating its high effectiveness in detecting objects after applying horizontal flip augmentation. Novelty: This research introduces novelty by employing the latest version of YOLO, YOLOv8, and comparing its performance with YOLOv8n, YOLOv8s, and YOLOv8m. The use of data augmentation techniques, such as horizontal flip, to increase data variation is also novel in expanding the dataset and improving the model's ability to recognize objects.
Implementation of face recognition using Python Christanto, Febrian Wahyu; Arifin, Husnul; Dewi, Christine; Prasandy, Teguh
Computer Science and Information Technologies Vol 7, No 1: March 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/csit.v7i1.p1-9

Abstract

Artificial intelligence (AI)-based technology systems are developing rapidly. Along with technological development the number of criminal cases caused by facial forgery is also growing. Cases of theft and housebreaking with fake photos are a common problem in Semarang. In 2022–2023 the number of cases of theft and housebreaking reached 372,965 with a crime risk level of 137/100,000 people. To overcome this problem the facial recognition system used in the door security system uses digital image processing. This method works by imitating how nerve cells communicate with interconnected neurons, or more precisely, how artificial neural networks function in humans. As training data, image capture and facial recognition are carried out using a webcam and the Python programming language with the TensorFlow library. The image processing algorithm uses 400 facial images with an accuracy rate of 95%. However further development is needed to improve the efficiency and accuracy of the system to produce better results.
Analysis of Consumer Purchasing Patterns Using the Apriori Algorithm on Sales Transaction Data from Anak Panah Kopi Salatiga Yoga Candra Adi Pratama; Christine Dewi
International Journal Software Engineering and Computer Science (IJSECS) Vol. 5 No. 1 (2025): APRIL 2025
Publisher : Lembaga Komunitas Informasi Teknologi Aceh (KITA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/ijsecs.v5i1.3275

Abstract

Anak Panah Coffee is a café located in Salatiga, offering a menu of 12 items. To enhance consumer satisfaction, the management of Anak Panah Coffee has decided to implement a marketing strategy for promoting its products. Given the challenges faced by Anak Panah Coffee, this study aims to analyze consumer preferences to provide benefits both to the business and its customers. This research utilizes the Apriori algorithm, based on field data that can be calculated objectively. The results of applying the Apriori algorithm reveal two association rules with a minimum support of 30% and a minimum confidence of 60%. The first rule indicates that customers who purchase Sunny Go Coffee are likely to also purchase Mushroom Crispy, with a support value of 50% and confidence of 56%. The second rule suggests that customers who buy Crispy Mushrooms are likely to also purchase Sunny Go Coffee, with a support value of 50% and a confidence of 71%.
Implementasi Metode YOLOv9 untuk Mendeteksi Pelanggaran Parkir di Bahu Jalan Perkotaan Adhi, Adeste Charisma Lumenvitha; Dewi, Christine
JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Vol 11, No 1 (2026)
Publisher : STKIP PGRI Tulungagung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29100/jipi.v11i1.7731

Abstract

Pelanggaran parkir merupakan salah satu faktor utama penyebab kemacetan lalu lintas di kawasan perkotaan. Penelitian ini bertujuan untuk mengembangkan sistem deteksi otomatis pelanggaran parkir berbasis algoritma YOLOv9, khususnya varian YOLOv9C yang mengutamakan efisiensi dan akurasi tinggi dalam pemrosesan real-time. Data pelatihan diperoleh dari frame video pengawasan yang telah dianotasi secara manual menjadi dua kategori: “Melanggar” dan “Tidak Melanggar”. Untuk meningkatkan generalisasi model terhadap kondisi lapangan, dilakukan teknik augmentasi data seperti rotasi, flipping, penyesuaian pencahayaan, dan mosaic augmentation. Model dilatih selama 50 epoch dan dievaluasi menggunakan metrik Confusion Matrix, Precision, Recall, F1-Score, dan Mean Average Precision (mAP). Hasil evaluasi menunjukkan bahwa YOLOv9C mampu mendeteksi pelanggaran dengan precision 0.995 dan mAP 0.822. Namun, ditemukan tantangan pada akurasi kelas minor akibat ketidakseimbangan data. Sistem ini berpotensi untuk diimplementasikan dalam skenario monitoring lalu lintas otomatis dengan dukungan edge computing. Rekomendasi pengembangan lanjutan meliputi integrasi metode segmentasi semantik dan balancing data untuk meningkatkan performa pada lingkungan kompleks.
OPTIMALISASI MODEL DETEKSI DINI DIABETES DENGAN TEKNIK FEATURES SELECTION Gunawan, Lanyta Setyani; Dewi, Christine
JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Vol 10, No 4 (2025)
Publisher : STKIP PGRI Tulungagung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29100/jipi.v10i4.7006

Abstract

Diabetes merupakan penyakit kronis yang dapat menyebabkan dampak serius jika tidak ditangani sejak dini, termasuk komplikasi seperti kerusakan organ dan penyakit kardiovaskular. Deteksi dini menggunakan teknologi machine learning menjadi salah satu kunci untuk pencegahan dan penanganan yang lebih efektif. Penelitian ini bertujuan untuk mengembangkan model prediksi risiko diabetes dengan menggunakan beberapa algoritma machine learning, seperti Random Forest, Naïve Bayes, Decision Tree, Logistic Regression, dan XGBoost. Dataset "Early Stage Diabetes Risk Prediction" dari UCI, yang terdiri dari 16 fitur dan 520 data, digunakan sebagai dasar pelatihan model. Beberapa teknik seleksi fitur, seperti Analisis Korelasi, Chi-Square, Information Gain, dan Fisher’s Score, diterapkan untuk mengidentifikasi fitur yang paling relevan dan mengurangi kompleksitas model. Evaluasi dilakukan menggunakan metrik seperti Accuracy, Precision, Recall, dan F1 Score. Hasil penelitian menunjukkan bahwa penerapan seleksi fitur secara signifikan meningkatkan performa model, menjadikannya jauh lebih baik dan akurat untuk mendukung deteksi dini risiko diabetes serta pengambilan keputusan medis yang lebih tepat dan responsif.
Co-Authors Adhi, Adeste Charisma Lumenvitha Aditya, Michael Rio Andika, Rio Arya Angkur, Lusiana V.G Anjar Widhyo Sasongko Aria Hendrawan, Aria Arifin, Husnul Bitra, Marcelino Bleskadit, Adri Agustinus Charmelita, Pauelina Chen, Rung-Ching Christanto, Henoch Juli Christo Sidupa, Bertnaldy Dagha, Willyam Chrisna Umbu Denny Jean Cross Sihombing Dienda Rizkya Hayuningtyas Roosaputri Emanuel Pabianan Faisal Rahutomo Febrian Wahyu Christanto Frans Robert Bethony Gerald Edgard Laukon Glorya Maya Marcia Sapan Bethony Gunawan, Lanyta Setyani Handayani, Sri Hendry Henoch Juli Christanto Henoch Juli Christanto Hiuredhy, Davin Kurnia Jhosefhin, Nicola Van Robert Juli Christanto, Henoch Julius Victor Manuel Bata Kroons, Aquenov Alexandro Kumala Nindya Pramono Lim, Ricardo Jonathan Mavish, Steven Nindya Pramono, Kumala Nindya Pramono Oktaviani, Gracelya Oky Dwi Nurhayati Pabianan, Emanuel Panja, Eben Patandung, Gabriel Pratama, Yoga Candra Adi Rahmat Gernowo Ria Cantika Larasati sangga, harmanto Stephen Aprius Sutresno, Stephen Aprius Tanujaya, Matthew Tappi, Liputra Pronimus Teguh Prasandy, Teguh Titis Handayani Valentina, Vierena Yeremia Yulianto Yerik Afrianto Singgalen Yoga Candra Adi Pratama Yulianto, Yeremia