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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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Penggunaan YOLOv8 untuk Deteksi Penyakit Daun Kopi Bitra, Marcelino; Dewi, Christine
Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen) Vol 5, No 4 (2024): Edisi Oktober
Publisher : LPPM STIKOM Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/kesatria.v5i4.501

Abstract

One of the products of plantation with a significant role in economic activities in Indonesia is coffee. But, coffee production in Indonesia is experienced a decline, where one of the causes is pest and disease attacks. Artificial intelligence can be a solution to help farmers detect diseases in coffee plants using object detection algorithm. This research uses the YOLOv8 object detection algorithm to carry out detection of the state and diseases of coffee plant leaves which are divided into four classifications, namely miner, rust, phoma and healthy. The research was conducted in three experimental scenarios which were differentiated based on a comparison of data distribution in the test set, validation set, and test set, where in sequence of train, validation, and test, the first scenario had a comparison of 80:10:10, the second scenario 70: 15:15, and third scenario 70:20:10. The research process using the YOLOv8s model got a model with the best performance results in data comparison of 70% train set, 20% validation set, and 10% test set. The best performing model has a mAP value of 97.8%, precision 95.2%, recall 96.6%, and f1-score 96%.
Analysis of Consumer Purchasing Patterns Using the Apriori Algorithm on Sales Transaction Data from Anak Panah Kopi Salatiga Pratama, Yoga Candra Adi; Dewi, Christine
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%.
Peramalan Jumlah Penerimaan Mahasiswa Baru Fakultas Teknik UKI Toraja dengan Metode Single Exponential Smoothing Glorya Maya Marcia Sapan Bethony; Christine Dewi; Frans Robert Bethony
Jurnal RESTIKOM : Riset Teknik Informatika dan Komputer Vol 6 No 3 (2024): Desember
Publisher : Program Studi Teknik Informatika Universitas Nusa Putra

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52005/restikom.v6i3.375

Abstract

This study examines the effectiveness of the Single Exponential Smoothing method in forecasting the number of new student admissions at the Faculty of Engineering, Christian University of Indonesia (UKI) Toraja. This study analyzes historical data on student admissions for 11 academic years, from 2013/2014 to 2023/2024, covering four study programs, namely Mechanical Engineering, Civil Engineering, Informatics Engineering, and Electrical Engineering. The research methodology includes problem identification, data collection, analysis using the Single Exponential Smoothing method, and accuracy testing with Mean Squared Error (MSE). This study tested three values of alpha (α) = 0.1, 0.5, and 0.9. The results showed that the use of alpha = 0.9 resulted in more accurate forecasting for all study programs. This level of accuracy is validated through the lowest MSE scores, namely Mechanical Engineering of 1450.61 (prediction of 159 students), Civil Engineering of 10890.23 (prediction of 147 students), Informatics Engineering of 4332.86 (prediction of 247 students), and Electrical Engineering of 674.66 (prediction of 49 students). The comparative graph analysis of the forecast results with the actual data is consistent showing that alpha = 0.9 produces the trend closest to the actual data for all study programs. The practical implications of this study include the potential to improve the accuracy of capacity planning, more efficient resource allocation, and improve the quality of engineering at the Faculty of Engineering, UKI Toraja. These results also highlight the importance of proper selection of alpha parameters in the Single Exponential Smoothing method to optimize forecasting accuracy.
SENTIMEN ANALISIS TERHADAP APLIKASI TIKTOK MENGGUNAKAN SUPPORT VECTOR CLASSIFICATION Christo Sidupa, Bertnaldy; Dewi, Christine
Jurnal Mnemonic Vol 8 No 1 (2025): Mnemonic Vol. 8 No. 1
Publisher : Teknik Informatika, Institut Teknologi Nasional malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36040/mnemonic.v8i1.12635

Abstract

Pemahaman terhadap persepsi pengguna aplikasi merupakan suatu aspek penting dalam pengembangan aplikasi. Sehingga menghasilkan informasi yang relefan dalam pengembangan aplikasi guna meningkatkan berbagai aspek kualitas pelayanan. Pendekatan yang digunakan dalam hal ini yakni menggunakan analisis sentimen. Perbandingan tiga metode dalam klasifikasi yang populer yakni Random Forest, Support Vector Machine (SVM), dan Naive Bayes merupakan topik utama dalam penelitian ini. Ulasan pengguna aplikasi TikTok di Google play store digunakan menjadi data dalam penelitian ini. Yang kemudian dimodifikasi menjadi sentimen positif, netral, dan negatif. Dalam proses ini melibatkan beberapa tahapan yakni, pre-processing data, pembobotan data menggunakan teknik TF-IDF, hingga penerapan metode klasifikasi. Selanjutnya dilakukaan evaluasi untuk mengukur kinerja setiap metode menggunakan metrik accuracy, precission, recall, dan F1-score. Dari hasil penelitian yang dilakukan teknik atau metode klasifikasi SVM mendapatkan hasil akurasi terbaik sebesar 85%, kemudian Random Forest 84%, dan Naive Bayes 83%. Selain mendapatkan hasil akurasi terbaik disisi lain SVM juga berhasil menunjukan stabilitas yang lebih baik dibandingkan metode yang lain. Penelitian ini menyimpulkan bahwa analisis sentimen terhadap aplikasi Tiktok menggunakan metode SVM lebih efektif. Hasil ini dapat memberikan value, baik dalam peningkatan aplikasi Tiktok serta peneltian selanjutnya dalam memilih metode yang terbaik dalam menganalisis data sentimen berbasis teks
Implementasi Sistem Pengambilan Nomor Antrean Online dengan Pendekatan Waterfall dan Keamanan MFA Bleskadit, Adri Agustinus; Dewi, Christine
Journal of Information System Research (JOSH) Vol 6 No 2 (2025): January 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v6i2.6187

Abstract

In the digital era, information technology plays an important role in increasing the efficiency of various sectors, including public services. One of the problems faced by the XYZ office in tax services is taking queue numbers. Long queues often cause long waiting times for visitors and reduce company efficiency, which ultimately impacts public satisfaction and perceptions of public services. An efficient queuing system not only improves the user experience but also the productivity of the institution. However, manual systems are often slow, prone to errors, and less flexible, so digital-based solutions are needed. This research aims to design a website-based queue number retrieval system using the waterfall method. To ensure the security of user data, the system is equipped with a Multi-Factor Authentication (MFA) feature, which increases the protection of user data from unauthorized access. This system was built using the PHP programming language and is supported by the XAMPP device as a local server. Tools such as Entity Relationship Diagrams (ERD) and Unified Modeling Language (UML) are used to design data structures and system flows effectively. It is hoped that this research will provide a practical solution to make it easier to collect queue numbers online, reduce waiting times, and increase user satisfaction and safety at the XYZ office.
Sistem Penyeleksi Penerima Bantuan Beras Miskin Kauman Kidul Menggunakan Metode Weighted Product Berbasis Mobile Christine Dewi; Yeremia Yulianto
Jurnal Teknik Informatika dan Sistem Informasi Vol 4 No 1 (2018): JuTISI
Publisher : Maranatha University Press

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

Abstract

The problem of poverty is one of the problems that has not been resolved yet in almost all developing countries such as Indonesia. The problem of poverty also affects the health education and income. The government has conducted various means to overcome the problem of poverty but it still has not been decreased yet because of a lot of errors in the distribution of assistance. Decision support system (DSS) is one of the methods to resolve the problem of poverty and errors in the distribution of assistance. Weighted Product methods (WP) is included of category of DSS which very suitable to select many of criteria that have provided by the government. WP method also gives results that are calculated very accurately and can be called as a method of ranking. This system becomes the solution and makes the distribution of assistance easier for administrative village officers. The system is used to select acceptance to help poor rice-based mobile. Using Weighted Product method, this system can run well proved by the valid result of comparison of Excel calculation with the calculation of existing algorithms in the system.
Quality of Service Analysis on the Steam Link Platform as an Alternative to Online Gaming Technology Patandung, Gabriel; Dewi, Christine
INOVTEK Polbeng - Seri Informatika Vol. 10 No. 2 (2025): July
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/rk0s3v40

Abstract

Cloud gaming is a modern gaming option that has emerged as a result of technological advancements, offering users the convenience of playing games without requiring high-end hardware. This study aims to analyze the Quality of Service (QoS) of the Steam Link platform as an alternative to traditional cloud gaming technology. The evaluation focuses on two types of clients—Android devices and laptops—using quantitative methods, benchmarking, and TIPHON standardization. The tested parameters include throughput, delay, frame rate, and the usage of CPU, GPU, and RAM. Experiments were conducted for 15 minutes across three game genres (FPS, Racing, and Open World), using resolutions of 720p and 1080p, and bandwidth levels of 30, 40, and 50 Mbps. Each scenario was tested three times. The host device used a PC with an Intel Core i5-6400 processor and GTX 1070 GPU, while the clients included a Xiaomi 12 smartphone and an Acer TravelMate i3-1115G4 laptop. Test results showed throughput ranging from 14.542 to 33.920 Mbps, delay between 1.382 and 1.721 ms, and frame rates stable between 30 and 60 FPS. CPU and RAM usage remained under 30%, indicating efficient performance. However, issues such as host stuttering and performance differences between clients were observed. According to TIPHON standards, both throughput and delay were rated as very good. With a stable 50 Mbps network connection, Steam Link proves to be a practical and affordable alternative for cloud gaming.
Classification of Skin Diseases Using YOLOv11 Tappi, Liputra Pronimus; Dewi, Christine
INOVTEK Polbeng - Seri Informatika Vol. 10 No. 2 (2025): July
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/9zv65764

Abstract

The skin, as the largest organ in the human body, is susceptible to various diseases that can be transmitted through direct contact or environmental exposure. Early detection of conditions such as cancer is crucial for effective treatment. This study implements the YOLOv11 algorithm to classify four types of skin diseases: Actinic Keratosis, Basal Cell Carcinoma, Melanocytic Nevus, and Melanoma. Using a Kaggle dataset of 2,000 images (500 per class), the images were processed by resizing them to 640×640 pixels and applying augmentation techniques (flipping, rotation, lighting adjustments) to enhance model robustness. The data was split into training (85%), validation (10%), and testing (5%). Model training on Google Colab (T4 GPU, 100 epochs) achieved an overall accuracy of 79%. Evaluation metrics showed strong results for Actinic Keratosis (precision=0.92, recall=0.92, F1=0.92) but lower performance for Melanoma (recall=0.59), likely due to class imbalance. Aggregate metrics indicated precision=0.80, recall=0.73, and F1=0.76, demonstrating reliable detection despite uneven performance across disease types. The main limitations include: a limited dataset size affecting model generalization; variability in image quality and lighting; and bias toward certain classes.
APRS and SSTV Technology for Audiovisual Data Transmission in Internet Blank Spot Areas to Increase the Effectiveness of SAR Activities Christanto, Febrian Wahyu; Handayani, Sri; Handayani, Titis; Dewi, Christine
Register: Jurnal Ilmiah Teknologi Sistem Informasi Vol 11 No 1 (2025): January
Publisher : Information Systems - Universitas Pesantren Tinggi Darul Ulum

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26594/register.v11i1.3205

Abstract

Volcanic eruptions can be detected through several warning signs. The Indonesian National Disaster Management Agency (BNPB) reported that between 2010 and 2021, Indonesia experienced 156 volcanic eruptions. The most recent occurred in 2021 when Mount Semeru erupted, forcing 10,395 people to evacuate, injuring 104, and causing 51 fatalities. The BNPB often experiences problems in carrying out mitigation, evacuation, rehabilitation, and reconstruction in disaster areas. On average, the search and evacuation process for victims takes about 3-7 days, so the probability of finding disaster victims is only about 50%. The proposed solution is a combination of radio transmission with Auto Packet Reporting System (APRS) technology as a medium for determining evacuation locations and Slow-Scan Television (SSTV) as a medium for transmitting audio and images of disaster sites, called Radio All-in-One (RAIONE). Using the Prototype method, this research has been tested for about 7 months with continuous improvements. The results show that the maximum distance covered is approximately 20 km with a minimum central antenna height of 7-10 meters, which increases the time effectiveness of SAR operations. The probability of finding survivors in a disaster increases to 75%, and SAR operations speed up to 1-2 days because of acceleration in the determination of search and evacuation locations in the Blank Spot Areas, reaching 91.30%.
Seminar and Workshop on Object Recognition using Deep Learning at Sam Ratulangi University Manado Dewi, Christine
Jurnal Pengabdian Masyarakat Vol. 5 No. 1 (2024): Jurnal Pengabdian Masyarakat
Publisher : Institut Teknologi dan Bisnis Asia Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32815/jpm.v5i1.1379

Abstract

Purpose: This seminar and workshop aim to address the lack of understanding among students regarding object recognition with deep learning. By exploring the concepts and applications of deep learning in object detection and recognition, participants will gain insights into this crucial aspect of computer vision. Method: The event will feature lectures, practical demonstrations, and hands-on workshops conducted by experts in the field. Participants will engage in interactive sessions to deepen their understanding of convolutional neural networks and other deep learning techniques for object recognition. Practical Applications: The knowledge gained from this seminar and workshop will have practical implications across various industries, including autonomous vehicles, healthcare, security systems, and robotics. Participants will learn how to apply deep learning algorithms to solve real-world problems related to object detection and recognition. Conclusion: By the end of the seminar and workshop, participants are expected to have acquired a deeper understanding of object recognition with deep learning and its practical applications. This will contribute to bridging the gap between theoretical knowledge and real-world implementation in the field of computer vision.
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