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All Journal Prosiding SNATIKA Vol 01 (2011) Jurnal Teknologi Informasi dan Ilmu Komputer International Journal of Advances in Intelligent Informatics The Asian Journal of Technology Management (AJTM) JOIV : International Journal on Informatics Visualization Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Network Engineering Research Operation [NERO] Jurnal Abdimas Mahakam Indonesian Journal of Electrical Engineering and Computer Science Frontier Energy System and Power Engineering JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika) Jurnal Teknik Informatika (JUTIF) Journal of Applied Data Sciences Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer) Prima Abdika: Jurnal Pengabdian Masyarakat Jurnal ilmiah teknologi informasi Asia
Sulistyo, Danang Arbian
Institut Teknologi Dan Bisnis ASIA Malang
Arts Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Public Health Social Sciences Other

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Pemodelan Fuzzy Inference System Tsukamoto untuk Prediksi Kejadian Banjir di Kota Malang Adipraja, Philip Faster Eka; Sulistyo, Danang Arbian; Wahyuni, Ida
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 7 No 1: Februari 2020
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

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

Abstract

Saat ini Malang menjadi kota yang mulai padat dengan perumahan penduduk. Hal tersebut mengakibatkan jumlah ruang terbuka hijau untuk penyerapan air hujan menjadi bekurang dan menyebabkan bencana banjir di beberapa tempat. Bencana banjir yang terjadi di Kota Malang merupakan bencana yang cukup serius dan membutuhkan penanganan cepat, karena banjir sering terjadi di perumahan padat penduduk. Oleh sebab itu, prediksi bencana banjir perlu dilakukan terlebih dahulu agar antisipasi dan mitigasi dapat dilakukan sedini mungkin. Tujuan dari penelitian ini adalah mengimplementasikan pemodelan algoritma Fuzzy Inference System (FIS) Tsukamoto untuk memprediksi terjadinya kejadian banjir di Kota Malang. Data yang digunakan adalah data curah hujan dan intensitas hujan di Kota Malang. Data tersebut diprediksi kedepannya sebagai masukan dalam memodelkan metode FIS Tsukamoto untuk memprediksi kejadian banjir dengan nilai error terkecil. Hasil prediksi yang dihasilkan oleh algoritma FIS Tsukamoto adalah jumlah kemungkinan kejadian banjir yang akan terjadi. Dari hasil pengujian yang dilakukan pada data jumlah kejadian banjir pada tahun 2016-2017 dihasilkan nilai error RMSE yang cukup kecil yaitu 2.76. Maka, dengan menggunakan data hasil perkiraan curah hujan dan intensitas hujan tiga tahun kedepan dari penelitian sebelumnya, pemodelan FIS Tsukamoto dapat diimplementasikan untuk memprediksi jumlah kejadian banjir di Kota Malang untuk tiga tahun kedepan mulai tahun 2018-2020. AbstractToday, Malang is a city that is starting to become crowded with population housing. This has resulted in the amount of green open space for absorption of rainwater to be reduced and causing floods in several places. The flood disaster that occurred in Malang City was a quite serious disaster and needed rapid handling, because flooding often occurs in densely populated housing. Therefore, the prediction of floods needs to be done in advance so that anticipation and mitigation can be done as early as possible. The purpose of this study is to implement the Tsukamoto Fuzzy Inference System (FIS) algorithm to predict the occurrence of flooding in Malang City. The data used are rainfall and rainfall intensity data in Malang City. The data is predicted in the future as input in modeling the Tsukamoto FIS method to predict flood events with the smallest error value. The prediction results generated by the Tsukamoto FIS algorithm are the number of possible flood events that will occur. From the results of the tests conducted on the data on the number of flood events in 2016-2017, the RMSE error value that was quite small was generated, which was 2.76. So, by using the results of rainfall and rainfall intensity estimates from the previous research, Tsukamoto's FIS modeling can be implemented to predict the number of flood events in Malang City for the next three years starting in 2018-2020.
ROUTING OPTIMIZATION ON SOFTWARE DEFINED NETWORK ARCHITECTURE USING BREADTH FIRST SEARCH ALGORITHM Armanda, David; Mukti, Fransiska Sisilia; Sulistyo, Danang Arbian
Jurnal Teknik Informatika (Jutif) Vol. 5 No. 4 (2024): JUTIF Volume 5, Number 4, August 2024 - SENIKO
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2024.5.4.2000

Abstract

Software Defined Network (SDN) is a network modelling that separates the control plane and data plane. SDN is a new form of paradigm used for large-scale networks, one of which is for routing. Most types of routing used today use single-path routing. Single-path only uses one path as data transmission. This will result in reduced performance on the network which is often referred to as network congestion. In this test, the routing algorithm used is Breadth First Search (BFS) by modifying the path so that congestion on the network can be minimised. The BFS algorithm is implemented using Mininet emulator, Ryu Controller, and fat-tree topology. In the test, 20 scenarios were used with a bandwidth of 50 - 1000 Mbps within 15 seconds. Tests were conducted to measure the performance of the BFS algorithm, namely the path and QOS (Quality Of Service) parameters which include testing delay, packet loss, jitter, and throughput. The data obtained in testing using the conventional BFS algorithm is compared with the modified BFS algorithm data in the same test method. In path testing, the modified BFS algorithm is superior and in parameter testing, it is produced with a degraded percentage value in delay (65%), packet loss (99%), jitter (84%), and throughput has increased by (41%). So the modified BFS algorithm is superior due to the utilisation of path modification for routing optimisation which is more effective in handling network congestion.
Perancangan dan Pembuatan Website Majelis Ulama Indonesia Kota Batu Malang Farokhah, Lia; Noercholis, Achmad; Ahda, Fadhli Almu’iini; Sulistyo, Danang Arbian; Rofiq, Muhammad
Prima Abdika: Jurnal Pengabdian Masyarakat Vol. 4 No. 1 (2024): Volume 4 Nomor 1 Tahun 2024
Publisher : Program Studi Pendidikan Guru Sekolah Dasar Universitas Flores Ende

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37478/abdika.v4i1.3746

Abstract

Community service is one of the activities required for lecturers at Institut Teknologi dan Bisnis ASIA Malang every semester. This activity is carried out in groups to distribute knowledge to the community. This activity is in partnership with the Batu City MUI in creating a digital website for distribution of information to the wider community. Problems arise when a partner's website is hacked or damaged by a hacker. The service team wanted to teach how to recover or mitigate after damage, but the technical team could not provide information regarding the website and suggested creating a new website. In the initial stage, this service will create a new website. The method of this service approach is to carry out discussions in group discussion forums (FGD). The results of the discussion were realized in the form of a website for the Batu City MUI. Evaluations were carried out regarding design and functionality requirements. The partners are satisfied but it must be developed further. In ongoing collaboration this website will continue to be developed. After that, training in mitigating data when exposed to hackers will be carried out in the next service.
Comparison of Adam Optimization and RMS prop in Minangkabau-Indonesian Bidirectional Translation with Neural Machine Translation Ahda, Fadhli Almu'iini; Wibawa, Aji Prasetya; Dwi Prasetya, Didik; Arbian Sulistyo, Danang
JOIV : International Journal on Informatics Visualization Vol 8, No 1 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.1.1818

Abstract

Language is a tool humans use to establish communication. Still, the language used is one language and between regions or nations with their languages. Indonesia is a country that has a diversity of second languages and is the fourth most populous country in the world. It is recorded that Indonesia has nearly 800 regional languages, but research activities in natural language processing are still lacking. Minangkabau is an endangered language spoken by the Minangkabau people in Indonesia's West Sumatra province. According to UNESCO, the Minangkabau language is listed as a language that is "definitely endangered," with only around 5 million speakers worldwide. This study uses neural machine translation (NMT) to create a formula based on this information. Neural machine translation, in contrast to conventional statistical machine translation, intends to build a single neural network that can be built up to achieve the best performance. Because it can simultaneously hold memory for a long time, comprehend complicated relationships in data, and provide information that is very important in determining the outcome of translation, LSTM is one of the most powerful machine-learning techniques for translating languages. The BLUE score is utilized in the NMT evaluation. The test results use 520 Minangkabau sentences, conducting tests based on the number of epochs ranging from 100-1000, resulting in optimization using Adam being better than optimization RMSprop. This is evidenced by the results of the best BLUE-1 score of 0.997816 using 1000 epochs.
Comparative Analysis of Ahmad-Yusoff and Jaro-Winkler Approaches for Javanese Language Stemming Andira, Aysza Belia Auly; Ahda, Fadhli Almu'iini; Sulistyo, Danang Arbian
Journal of Information System Research (JOSH) Vol 7 No 2 (2026): January 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v7i2.7879

Abstract

This research presents a performance comparison between two approaches for identifying the base form of affixed Javanese words: the Ahmad Yusoff Sembok (AYS) rule-based stemming algorithm and the Jaro-Winkler (JW) string similarity approach. Javanese was selected as the focus because of its complex morphological structure, encompassing prefixes, suffixes, infixes, and confixes, along with significant speech-level and dialectal variation, which together pose challenges for natural language processing. The dataset comprises 720 manually annotated word lemma pairs. Evaluation was carried out using precision, recall, F1-score, accuracy, and Cohen’s Kappa, complemented by error analysis on over-stemming and under-stemming cases. Results indicate that JW achieves higher overall performance (83.19% accuracy, 83% F1-score) compared to AYS (73.19% accuracy, 73% F1-score), with AYS producing more over-stemming errors (88 cases) and JW showing more under-stemming errors (47 cases). These outcomes suggest that similarity-based approaches are more effective in addressing Javanese morphological complexity, while also contributing a benchmark dataset of manually annotated Javanese word lemma pairs, a comparative evaluation framework between rule-based and similarity-based approaches, and practical insights for the development of stemming tools in regional languages that currently lack NLP resources.
Analisis Sentimen Kebijakan Makan Bergizi Gratis Menggunakan IndoBERT dan Machine Learning Sulistyo, Danang Arbian; Setiadi, Erik
JURNAL FASILKOM Vol. 15 No. 3 (2025): Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer)
Publisher : Unversitas Muhammadiyah Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/jf.v15i3.10546

Abstract

Media sosial telah menjadi forum vital untuk opini publik terhadap kebijakan pemerintah, seperti program "Makan Bergizi Gratis" (MBG) di Indonesia. Memahami sentimen ini sangat penting bagi pemangku kepentingan. Penelitian ini bertujuan untuk (1) menganalisis distribusi sentimen publik terhadap kebijakan MBG dan (2) menentukan model machine learning terbaik untuk klasifikasi sentimen tersebut. Penelitian ini menggunakan 12.389 tweet yang dikumpulkan dari platform X. Metode hybrid labeling, yang mengkombinasikan leksikon berbasis domain dengan IndoBERT, diterapkan untuk melabeli data secara otomatis. Untuk klasifikasi, tiga model (Random Forest, XGBoost, dan Ensemble) dibandingkan menggunakan fitur hybrid (TF-IDF trigram, embedding IndoBERT, dan fitur leksikon) pada dataset yang telah diseimbangkan dengan SMOTE. Hasil penelitian menunjukkan bahwa sentimen publik didominasi oleh sentimen negatif (68,6%), diikuti oleh positif (19,5%) dan netral (11,9%). Model Random Forest menunjukkan kinerja tertinggi, dengan pencapaian F1-Score rata-rata 0.9383 pada K-Fold cross-validation dan 0.9363 pada test set final. Studi ini membuktikan bahwa pendekatan hybrid yang diusulkan sangat efektif untuk klasifikasi sentimen publik berbahasa Indonesia pada domain kebijakan pemerintah.
Analisis Sentimen Ulasan Pemain Genshin Impact Menggunakan Kombinasi TF-IDF, Lexicon, dan Support Vector Machine Sulistyo, Danang Arbian; Fahrudillah, Mochammad Fiqi
JURNAL FASILKOM Vol. 15 No. 3 (2025): Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer)
Publisher : Unversitas Muhammadiyah Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/jf.v15i3.10553

Abstract

The rapid growth of the digital gaming industry in Indonesia has been accompanied by a significant increase in user-generated reviews on distribution platforms such as Google Play Store. This condition necessitates automated methods capable of efficiently interpreting player perceptions on a scale. This study conducts sentiment analysis on player reviews of Genshin Impact by developing a seven-stage analytical pipeline consisting of data preparation, lexicon-based labeling, TF-IDF feature extraction, Support Vector Machine (SVM) training, multi-metric evaluation, rule-based post-processing, and automated summarization using a Large Language Model. A total of 40,000 reviews from 2023 until 2025 were collected through web scraping and processed through text cleaning, slang normalization, tokenization, stopword removal, and stemming. Initial labels were generated using an updated domain-specific sentiment lexicon and subsequently refined through a rule-patch mechanism that handles negation, contrastive expressions, and domain-specific technical cues such as lag, bug, and crash. The SVM model was trained using a TF-IDF configuration (1–3 grams) and evaluated across 10 runs with different random seeds, producing an average accuracy of 0.945, a macro-F1 of 0.900, and stable performance across iterations. Visualization of sentiment distribution and WordClouds highlights prominent thematic patterns within each class, while automated summarization using IBM Granite provides qualitative insights into player appreciation of visual and character design, alongside complaints related to performance issues and the game’s gacha system. Overall, the integration of statistical, rule-based, and LLM-driven approaches demonstrates an effective and contextually robust framework for sentiment analysis in game analytics
Co-Authors A.N. Afandi Achmad Noercholis Aji Prasetya Wibawa Andika Dwiyanto, Felix Andira, Aysza Belia Auly Andrew Nafalski Aripriharta - Armanda, David Didik Dwi Prasetya Fadhli Almu’iini Ahda Fahrudillah, Mochammad Fiqi Farokhah, Lia Fransiska Sisilia Mukti Gunawan Gunawan Hanip Afandi I Nyoman Gede Arya Astawa Ida Wahyuni Lilis Widayanti Machrus Tohir Machrus Tohir Moh Ashari Muhammad Rofiq Muhammad Rofiq Philip Faster Eka Adipraja Pratama, Ficho Pranandasya Andrian Setiadi, Erik Siti Maharani, Farah Shafiyah Suastika Yulia Riska Sulthan Shidqi Sulthan Shidqi Vivi Aida Fitria Wayan Firdaus Mahmudy Yogie Susdyastama Putra