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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] 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 Jurnal Abdimas Mahakam
Sulistyo, Danang Arbian
Institut Teknologi Dan Bisnis ASIA Malang
Arts Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Energy Engineering Environmental Science Languange, Linguistic, Communication & Media Mathematics Social Sciences Other

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Pembuatan Infrastruktur Database Menggunakan Metode Replikasi Untuk Pelanggan Jagoan Hosting Sulthan Shidqi; Danang Arbian Sulistyo; Fadhli Almu’iini Ahda
Jurnal Ilmiah Teknologi Informasi Asia Vol 16 No 1 (2022): Volume 16 Nomor 1 (8)
Publisher : LP2M INSTITUT TEKNOLOGI DAN BISNIS ASIA MALANG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32815/jitika.v16i1.702

Abstract

ABSTRAK Penelitian ini membahas penggunaan dan penerapan cluster database untuk mengurangi dampak buruk pada website akibat server mengalami downtime, sehingga dapat tetap menjaga trafik pengunjung yang sedang beraktivitas pada website. Pada penelitian ini akan dilakukan implementasi perancangan cluster database dan pengujian hasil dari implemetasi cluster database pada server. Dari hasil tes tersebut akan ditemukan dampak yang ditimbulkan dengan adanya cluster database pada server supaya dapat digunakan sebagai refrensi pada pembuatan infrastruktur sebuah website. Dalam pengujian program dilakukan dengan membandingkan dari hasil system sebelum dilakukan implementasi cluster dan setelah dilakukan implementasi cluster. Dalam uji coba yang telah dilakukan mencapai hasil yang sesuai tidak terjadi downtime pada akses website apabila terjadi kegagalan pada salah satu server database. Dari hasil tersebut menunjukan bahwa cluster database berfungsi dengan baik dalam menjaga uptime website yang ada. Kata kunci: Cluster, Database, Infrastruktur, Website
Analisis Penempatan Access Point Pada Jaringan Wireless LAN STMIK Asia Malang Menggunakan One Slope Model Mukti, Fransiska Sisilia; Sulistyo, Danang Arbian
Jurnal Ilmiah Teknologi Informasi Asia Vol 13 No 1 (2019): Volume 13 Nomor 1 (8)
Publisher : LP2M Institut Teknologi dan Bisnis ASIA Malang

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Abstract

Penelitian ini bertujuan untuk menganalisis penempatan access point (AP) pada jaringan WLAN STMIK Asia Malang, yang berdampak terhadap level daya atau kuat sinyal yang diterima oleh pengguna. Pendekatan pertama melalui site survey, dengan tujuan yakni mendapatkan informasi yang cukup mengenai jumlah dan penempatan AP yang saat ini diaplikasikan pada gedung kampus STMIK Asia Malang. Hasil dari walktest ini akan digunakan sebagai parameter untuk perhitungan teoritis menggunakan model propagasi One Slope Model (1SM). Berdasarkan perhitungan 1SM, didapatkan jarak optimal untuk penempatan AP tidak lebih dari 13 m pada propagasi LOS (rentang kuat sinyal -10dB sampai dengan -20dB, pada area koridor gedung) dan jarak 6 m pada propagasi NLOS (rentang kuat sinyal -40dB sampai dengan -50dB, pada area ruangan perkuliahan). Hasil analisis membuktikan bahwa keberadaan barrier mempengaruhi kekuatan sinyal yang diterima oleh user, sehingga penempatan perangkat WLAN, dalam hal ini AP perlu diperhatikan.
An enhanced pivot-based neural machine translation for low-resource languages Sulistyo, Danang Arbian; Wibawa, Aji Prasetya; Prasetya, Didik Dwi; Ahda, Fadhli Almuíini
International Journal of Advances in Intelligent Informatics Vol 11, No 2 (2025): May 2025
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v11i2.2115

Abstract

This study examines the efficacy of employing Indonesian as an intermediary language to improve the quality of translations from Javanese to Madurese through a pivot-based approach utilizing neural machine translation (NMT). The principal objective of this research is to enhance translation precision and uniformity among these low-resource languages, hence advancing machine translation models for underrepresented languages. The data collecting approach entailed extracting parallel texts from internet sources, followed by pre-processing through tokenization, normalization, and stop-word elimination algorithms. The prepared datasets were utilized to train and assess the NMT models. An intermediary phase utilizing Indonesian is implemented in the translation process to enhance the accuracy and consistency of translations between Javanese and Madurese. Parallel text corpora were created by collecting and preprocessing data, thereafter, utilized to train and assess the NMT models. The pivot-based strategy regularly surpassed direct translation regarding BLEU scores for all n-grams (BLEU-1 to BLEU-4). The enhanced BLEU ratings signify increased precision in vocabulary selection, preservation of context, and overall comprehensibility. This study significantly enhances the current literature in machine translation and computational linguistics, especially for low-resource languages, by illustrating the practical effectiveness of a pivot-based method for augmenting translation precision. The method's dependability and efficacy in producing genuine translations were proved through numerous studies. The pivot-based technique enhances translation quality, although it possesses limitations, including the risk of error propagation and bias originating from the pivot language. Further research is necessary to examine the integration of named entity recognition (NER) to improve accuracy and optimize the intermediate translation process. This project advances the domains of machine translation and the preservation of low-resource languages, with practical implications for multilingual communities, language education resources, and cultural conservation.
Minangkabau Language Stemming: A New Approach with Modified Enhanced Confix Stripping Ahda, Fadhli Almu'iini; Aji Prasetya Wibawa; Didik Dwi Prasetya; Danang Arbian Sulistyo; Andrew Nafalski
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 9 No 3 (2025): June 2025
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v9i3.6511

Abstract

Stemming is an essential procedure in natural language processing (NLP), which involves reducing words to their root forms by eliminating affixes, including prefixes, infixes, and suffixes. The employed method assesses the efficacy of stemming, which differs according to language. Complex affixation patterns in Indonesian and regional languages such as Minangkabau pose considerable difficulties for traditional algorithms. This research adopts the enhanced fixed-stripping method to tackle these issues by integrating linguistic characteristics unique to Minangkabau. This study has three phases: data acquisition, pseudocode development, and algorithm execution. Testing revealed an average accuracy of 77.8%, indicating the algorithm's proficiency in managing Minangkabau’s intricate morphology. Nevertheless, constraints persist, particularly with irregular affixation patterns. Possible improvements could include adding more datasets, improving the rules for handling affixes, and using machine learning to make the system more flexible and accurate. This study emphasizes the significance of customized solutions for regional languages and provides insights into the advancement of NLP in various linguistic environments. The findings underscore the progress made in processing Minangkabau text while also emphasizing the need for further research to address current issues.
High-accuracy classification of banana varieties using ResNet-50 and DenseNet-121 architectures Riska, Suastika Yulia; Sulistyo, Danang Arbian; Siti Maharani, Farah Shafiyah
Indonesian Journal of Electrical Engineering and Computer Science Vol 39, No 1: July 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v39.i1.pp322-335

Abstract

Bananas are a popular fruit in Indonesia due to their affordability, availability, and rich nutritional content. Identifying different banana types is crucial for consumption and processing, yet some types are difficult to distinguish visually. This study aims to classify banana types using convolutional neural network (CNN) architectures, specifically ResNet-50 and DenseNet-121. The dataset consists of five banana classes, which were processed using preprocessing techniques to enhance image quality prior to model training. The results demonstrate that the proposed models can classify banana types with high accuracy. The research methodology includes data collection, preprocessing, CNN model implementation, and performance evaluation using a confusion matrix. The dataset was split into training and testing sets in an 80:20 ratio, with validation data extracted from the training set in a 90:10 ratio. The models were trained on the training data, validated with validation data, and tested on the testing data to assess final performance. The study concludes that the CNN architectures employed are effective in classifying banana types, with the DenseNet-121 model achieving 93.02% accuracy, outperforming the ResNet-50 model, which achieved 92.44%. These results indicate that the models can capture essential features from banana images and produce accurate predictions.
Peningkatan Akurasi Deteksi Intrusi Jaringan dengan Model Hybrid Convolutional Neural Network dan Long Short-Term Memory Pratama, Ficho Pranandasya Andrian; Sulistyo, Danang Arbian; Mukti, Fransiska Sisilia
Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika) Vol 10, No 2 (2025): Edisi Agustus
Publisher : STIKOM Tunas Bangsa Pematangsiantar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/jurasik.v10i2.895

Abstract

The evolving cyber threats demand more sophisticated and accurate intrusion detection systems (IDS). This research develops a hybrid CNN-LSTM model with comprehensive data preprocessing techniques to enhance network attack detection accuracy. The UNSW-NB15 dataset consisting of nine attack categories and 49 features was used as research data. The methodology begins with data preprocessing including data cleaning, categorical transformation using categorical codes, class balancing with upsampling, StandardScaler normalization, and 80:20 data splitting. The hybrid model architecture combines three CNN blocks for spatial feature extraction with two LSTM layers for modeling temporal dependencies. The model was compiled using Adam optimizer with 0.0005 learning rate and equipped with EarlyStopping, ReduceLROnPlateau, and ModelCheckpoint callbacks. Evaluation results show the CNN-LSTM model achieves 99% accuracy, precision, recall, and F1-score, significantly outperforming the standard CNN model which only reaches 96%. Learning curves demonstrate rapid convergence without overfitting indication. This research proves that the combination of CNN's spatial feature extraction capability and LSTM's temporal dependency modeling is highly effective for anomaly detection in complex sequential data such as network traffic.
Multilingual Parallel Corpus for Indonesian Low-Resource Languages Sulistyo, Danang Arbian; Wibawa, Aji Prasetya; Prasetya, Didik Dwi; Ahda, Fadhli Almu’iini; Arya Astawa, I Nyoman Gede; Andika Dwiyanto, Felix
JOIV : International Journal on Informatics Visualization Vol 9, No 5 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.5.3412

Abstract

Indonesia has an extraordinary number of languages, with more than 700 regional languages such as Javanese, Madurese, Balinese, Sundanese, and Bugis. Despite the wealth of languages, digital resources for these languages remain scarce, making the preservation and accessibility of digital languages a significant challenge. Research was conducted to address this gap by building a multilingual parallel corpus consisting of more than 150,000 phrase pairs extracted from Bible translations in five regional languages in Indonesia. Rigorous preprocessing, normalization, and Unicode tokenization were performed to improve data quality and consistency. The encoder-decoder architecture was a key focus in the development of the NMT model. Evaluation focused on forward and backward translation directions, which were measured using BLEU scores. The results show that forward translation consistently outperforms backward translation. The Indonesian Javanese model produced a score of 0.9939 for BLEU-1 and 0.9844 for BLEU-4, indicating a high level of translation quality. In contrast, reverse translation tasks, such as translating from Sundanese to Indonesian, presented significant challenges, with BLEU-4 scores as low as 0.3173. This illustrates the complexity of the translation system from Indonesian to local languages. If future research focuses on transformer-based models and incorporates additional linguistic parameters to enhance the accuracy of natural language processing (NLP) models for Indonesia's underrepresented regional languages, this work provides a dataset that can be utilized for that purpose.
Empowering Low-Resource Languages: Javanese Machine Translation Sulistyo, Danang Arbian; Aji Prasetya Wibawa; Wayan Firdaus Mahmudy; Fadhli Almu’iini Ahda; Andrew Nafalski
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 9 No 5 (2025): October 2025
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v9i5.6887

Abstract

This study addresses the critical need to preserve and revitalize the Javanese language, which despite its widespread popularity, faces challenges as a low-resource language in Indonesia. The decline in Javanese proficiency among younger generations poses a significant threat to the language's cultural significance and heritage. To address this issue, this study introduces an innovative approach to machine translation, focusing on the development of a robust Indonesian-Javanese translation system. Utilizing advanced neural machine translation (NMT) techniques, including Long Short-Term Memory (LSTM) networks, the proposed system aims to bridge the linguistic gap between Indonesian and Javanese. Special attention was given to the unique linguistic characteristics and challenges of Javanese, with the goal of achieving exceptional translation accuracy and fluency. Through extensive experimentation and evaluation, this study aims to demonstrate the effectiveness of the translation system in facilitating cross-cultural communication and language preservation efforts within the Javanese-speaking community. By emphasizing the significance of Javanese as a widely spoken yet under-resourced language, this study underscores the importance of innovative technological solutions in safeguarding linguistic diversity and cultural heritage. Through its contributions, the research seeks to address the pressing need for language preservation and revitalization, particularly in the context of low-resource languages like Javanese.
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

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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.
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 Rabbani, Muhammad Faruqi Setiadi, Erik Siti Maharani, Farah Shafiyah Suastika Yulia Riska Sulthan Shidqi Sulthan Shidqi Tampubolon, Surya Viari Vivi Aida Fitria Wayan Firdaus Mahmudy Yogie Susdyastama Putra