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All Journal International Journal of Electrical and Computer Engineering Semantik Jurnal Buana Informatika TELKOMNIKA (Telecommunication Computing Electronics and Control) Bulletin of Electrical Engineering and Informatics Sistemasi: Jurnal Sistem Informasi Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) SemanTIK : Teknik Informasi JURNAL MEDIA INFORMATIKA BUDIDARMA Jurnal Sains dan Informatika JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI Jurnal Sisfokom (Sistem Informasi dan Komputer) Technomedia Journal Jiko (Jurnal Informatika dan komputer) Indonesian Journal of Computing and Modeling International Journal of Natural Science and Engineering JURTEKSI Jurnal Informatika JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) JUSIM (Jurnal Sistem Informasi Musirawas) Building of Informatics, Technology and Science Progresif: Jurnal Ilmiah Komputer Journal of Information Systems and Informatics Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi) Aiti: Jurnal Teknologi Informasi MEANS (Media Informasi Analisa dan Sistem) Jurnal Teknik Informatika (JUTIF) Journal of Information Technology Ampera Edutik : Jurnal Pendidikan Teknologi Informasi dan Komunikasi International Journal Software Engineering and Computer Science (IJSECS) INOVTEK Polbeng - Seri Informatika
Indrastanti Ratna Widiasari
Universitas Kristen Satya Wacana
Agriculture, Biological Sciences & Forestry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Education Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media Mathematics Other

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Design and Implementation of Load Balancing for Quality of Service Improvement Indrastanti Ratna Widiasari; Rissal Efendi
Jurnal Buana Informatika Vol. 15 No. 2 (2024): Jurnal Buana Informatika, Volume 15, Nomor 02, Oktober 2024
Publisher : Universitas Atma Jaya Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24002/jbi.v15i2.9530

Abstract

At the Information Technology Faculty, Satya Wacana Christian University, load balancing systems are implemented where the web server serves 500 users. This is to prevent server overload or downtime during simultaneous access to the web server. Test results indicate significant differences in CPU usage, request time, and bandwidth between load balancing and single servers. The use of load balancing is more effective than relying on a single server, as evidenced by test results. The CPU usage with load balancing is significantly lower, with a difference of up to 45% compared to a single server. The request time with load balancing is also slightly better, with only 21.5ms compared to 42ms for a single server. However, the difference in bandwidth between load balancing and a single server is not very significant. The highest bandwidth recorded on a single server is 182kb/s, while with load balancing it reaches 165kb/s.
Analysis of Smart City Quick Win Program Implementation Using Fuzzy BWM and TOPSIS in Manado and Tomohon Puteri Justia Kardia Momuat Wahani; Sri Yulianto Joko Prasetyo; Indrastanti R. Widiasari; Johan J. C. Tambotoh
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol. 15 No. 3 (2026): JULY
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v15i3.2690

Abstract

This study analyzes the implementation quality of smart city quick win programs in Manado and Tomohon using the Fuzzy Best-Worst Method (BWM) and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). The evaluation covers 12 quick win programs across six smart city dimensions: smart governance, smart branding, smart economy, smart living, smart society, and smart environment. The assessment uses 12 criteria derived from government smart city evaluation elements, including policy alignment, problem and goal clarity, public benefit, implementation readiness, technological readiness, governance ownership, service performance, monitoring and evaluation, and sustainability. Data were collected from official documents, evidence catalogs, interviews, expert assessments from 20 respondents, and government evaluation records. The novelty of this study lies in the development of a program-level evaluation model that integrates Fuzzy BWM for criteria weighting and TOPSIS for ranking quick win programs. Unlike previous studies that mainly evaluate smart city performance at the city or dimension level, this study positions quick win programs as concrete implementation units. The results show that problem and goal clarity, direct public benefit, and service performance are the most influential aspects in determining implementation quality. The TOPSIS results identify Manado 360 and SmartGov/PONTER as the strongest programs, while the city-level comparison shows that Manado has more consistent implementation quality than Tomohon. This study contributes a structured decision-support model for evaluating and improving smart city quick win programs.
Accuracy Evaluation of the Naïve Bayes Classifier for Sentiment Classification of Diploma Authenticity Issues using Orange Eunike Loise Laapen; Indrastanti Ratna Widiasari
SISTEMASI Vol 15, No 6 (2026): Sistemasi: Jurnal Sistem Informasi
Publisher : Universitas Islam Indragiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v15i6.6585

Abstract

The rapid growth of digital text data has increased the demand for effective methods to extract meaningful information, particularly for understanding public opinion. Sentiment analysis is widely used to classify opinions into positive, negative, and neutral categories. However, challenges such as linguistic ambiguity, subjectivity, and class imbalance often degrade classification performance. This study aims to evaluate the performance of the Naïve Bayes algorithm for sentiment classification on the issue of diploma authenticity using a publicly available dataset, while examining the impact of data distribution on model performance. A quantitative experimental approach was employed using an original dataset of 1,014 instances and an oversampled dataset of 1,767 instances. The data were processed through preprocessing, Bag-of-Words feature extraction, and sentiment classification using Orange Data Mining with 10-fold cross-validation. Model performance was evaluated using accuracy and the Area Under the Receiver Operating Characteristic Curve (AUC). The results indicate that the Naïve Bayes model achieved an accuracy of 37.2% and an AUC of 0.704 on the original imbalanced dataset, reflecting relatively poor classification performance. After applying oversampling to balance the class distribution, the model's accuracy increased substantially to 82.1%, while the AUC improved to 0.970. These findings demonstrate that class distribution has a significant impact on the performance of the Naïve Bayes algorithm in sentiment classification and highlight the importance of addressing class imbalance to achieve more reliable classification results.
Real-time flood forecasting with attention-enhanced hybrid deep learning using internet of things data Rissal Efendi; Indrastanti R. Widiasari
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 24, No 3: June 2026
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v24i3.27501

Abstract

Floods are a frequent disaster in Semarang city, Indonesia, requiring an accurate and real-time forecasting system to support effective risk management. This study introduces a hybrid long short-term memory-gated recurrent unit (LSTM-GRU) model with an attention mechanism (attention-enhanced LSTM-GRU) designed to improve the accuracy of flood predictions based on multiparameter internet of things (IoT) data. The novelty of this study lies in the integration of the attention mechanism within the hybrid LSTM-GRU architecture, which allows the model to provide adaptive focus on features and time periods that most influence flood occurrences. The dataset used consists of 1,736 time series samples covering rainfall and water level data collected every 15 minutes from IoT sensors in the upstream and downstream areas of Semarang, Indonesia. Experimental results show that the hybrid model with the attention mechanism provides the best performance with a mean absolute percentage error (MAPE) value of 1.4%, root mean squared error (RMSE) of 1.05, and coefficient of determination (R²) reaching 0.96. This model also achieves 100% recall for the “Danger” class, demonstrating its reliability in detecting critical conditions. The practical implication of this research is the availability of a flood prediction model that is accurate, adaptive, and can be directly applied to IoT-based early warning systems in flood-prone urban areas.
Optimizing a Hybrid Deep Learning Model for DDoS Detection Using DBSCAN and PSO Indrastanti Ratna Widiasari; Rissal Efendi
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 9 No 6 (2025): December 2025
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v9i6.6383

Abstract

This study proposes a hybrid deep learning approach that combines Gated Recurrent Units (GRUs) and Convolutional Neural Networks (CNNs) for Distributed Denial of Service (DDoS) cyberattack detection. The model, called DBSCAN–GRU–CNN, uses density-based clustering (DBSCAN) to select relevant features and reduce execution time. The dataset for this study was obtained from live penetration testing, where a series of simulated attacks was performed on a monitored network. To evaluate the performance of the proposed model, several comparison models were used, including DBSCAN–GRU–CNN (Single Hidden Layer), DBSCAN–GRU–CNN (Double Hidden Layers), DBSCAN–GRU–CNN (With Regularization), DBSCAN–GRU–CNN–PSO, GRU–CNN, GRU–CNN (With Hyperparameter Tuning), and Random Forest (Tuned Model). Variations of the model tested were made by adding hidden layers, regularization, optimization with Particle Swarm Optimization (PSO), and hyperparameter tuning. Experimental results show that the DBSCAN–GRU–CNN–PSO model provided optimal performance with a 99.3% accuracy, a 99% precision, a 98.9% recall, and a 99% F1-score, while the model with hyperparameter tuning achieved a 99% accuracy. By adding PSO, the model achieved optimized weights, better generalization, and excellent accuracy in DDoS detection.
Analysis of WAN Network Reliability Based on Response Time and Downtime at the Faculty of Information Technology UKSW Kevin; Indrastanti R. Widiasari
Journal of Innovation and Technology Polbeng Series on Informatics (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: 10.35314/7bdz0a54

Abstract

Wide Area Network reliability is crucial in supporting academic and administrative activities in higher education institutions. This study aims to evaluate the reliability of the WAN network at the Faculty of Information Technology, UKSW, using response time and Downtime as the main indicators. The research employed a quantitative descriptive approach by utilizing PRTG Network Monitor, Ping, and Zabbix to measure network performance. The results showed that the average response time was 104.31 ms, with a maximum response time of 614.0 ms. The total Downtime recorded was 22 hours and 42 minutes, with a network uptime percentage of 80.16%. These findings indicate that while the network remains operational, optimization is needed to reduce latency fluctuations and minimize Downtime. Recommendations include enhancing network infrastructure and implementing proactive monitoring strategies.
Perancangan IoT Monitoring Lingkungan Berbasis Wireless Sensor Network (WSN) Dengan Menerapkan Multi Sensor Network (MSN) Rega Dhiwastu Prasetia; Indrastanti Ratna Widiasari
JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Vol 10, No 1 (2025)
Publisher : STKIP PGRI Tulungagung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29100/jipi.v10i1.6040

Abstract

Makalah ini menerapkan IoT berbasis Wireless Sensor Network sebagai pemantauan lingkungan yang juga menerapkan sistem Multi Sensor Network yang dapat diakses secara real-time . Sistem penelitian ini dirancang dengan menggunakan perangkat elektronik berupa modul sensor DHT22 sebagai sensor suhu dan kelembaban, modul sensor MQ135 sebagai sensor gas, modul Flame Sensor sebagai pendeteksi api, modul sensor SW420 untuk mendeteksi adanya getaran, modul Raindrops Sensor sebagai sensor curah hujan untuk mendeteksi adanya hujan. , serta papan ESP32 sebagai mikrokontroler pengontrol pada program. Pada penelitian ini metode yang digunakan adalah Penelitian dan Pengembangan dengan merancang membangun perangkat Jaringan Sensor Nirkabel sebagai perangkat pemantauan lingkungan dengan membaca menggunakan beberapa sensor secara real-time menggunakan jaringan internet. Kinerja dari sensor tersebut adalah mengumpulkan data di lingkungan sekitar dan dikirimkan ke mikrokontroler serta akan ditampilkan melalui aplikasi Blynk IoT. Penelitian ini diharapkan dapat membantu dalam pemantauan kondisi lingkungan secara efisien dan efektif, memberikan data yang akurat dan dapat diandalkan untuk analisis lebih lanjut.
Co-Authors Aditya Indra Cahya Amanda Angreyni Regal Ardianto, Yeheskiel Hency Arnando, Kelvin Arya Pradana Bagus Pramono Bayu Edhi Setyawan Billy Hamsen Cahyaningrum, Yuliana Cahyaningtyas, Christian Christopher, William Cristianto, Denil Dian W Chandra Eko Sediyono Eunike Loise Laapen Fra Arsandy Kusuma Sejati Fransiskus Simson Irwan Sembiring Johan J. C. Tambotoh Jonathan Liando Kelvin Arnando Kendek Allo, Alvin Kevin Kevin Lala, Zefanya Loudewieq Gabriel Maay, Buyung Herfandy Maitimu, Excel Barthel Manuputty, Yandri Y Marpaung, Kristian Vieri Merryana Lestari Najoan, Patrick Nikolaus Toni Patrick Najoan Prahara, Imanuel Noval Amanda Puteri Justia Kardia Momuat Wahani Rapawuli Eko Putro Raweyai, Soni Sampari Rega Dhiwastu Prasetia Rezky Kristian Bangun Rissal Efendi Rissal Efendi Rissal Efendi Rissal Effendi Riyanto, Muhamad Rizal Dwi Pratomo safry pasau tangdisosang Sebayang, Andreas Setiawan, Muhamad Avif Simarmata, Hotman Samuel Sri Yulianto Joko Prasetyo Sufardy, Devander Benaryanta Suwijo Danu Prasetyo T. Arie Setiawan Prasida Teguh Wahyono Theopillus J. H. Wellem Theovan Gracia Stefana Putra Ulfa Septilia Permatasari Wibowo, Gading Haryono Wisnu Widi Anggoro Yessica Nataliani Yoel Chandra Eka Paksi Yonatan Surya Kristama