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All Journal Techno.Com: Jurnal Teknologi Informasi MATICS : Jurnal Ilmu Komputer dan Teknologi Informasi (Journal of Computer Science and Information Technology) Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control JISKa (Jurnal Informatika Sunan Kalijaga) ILKOMNIKA: Journal of Computer Science and Applied Informatics
Okta Qomaruddin Aziz
Teknik Informatika, Fakultas Sains Dan Teknologi, Universitas Islam Negeri Maulana Malik Ibrahim Malang
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Energy Engineering Library & Information Science

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Performance improvement in Resampling Based Clustering Aziz, Okta Qomaruddin
MATICS Vol 12, No 1 (2020): MATICS
Publisher : Department of Informatics Engineering

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (402.734 KB) | DOI: 10.18860/mat.v12i1.8918

Abstract

Clustering is one of powerful technique to find a biological mechanism in gene expression. This technique identify a gene that has same expression. Using bootstrap method we can improve the quality of microarray, thus resampling based clustering (RC) is consider one of the improvement. RC use K-means clustering to determine initial parameter and need thousands of iteration to converge. Performance improvement can be done at preprocess, such as normalization and changing the initial parameter. Normalization can remove or lower the bias in microarray. The result show that normalization can improve the accuracy of RC. In addition, for parameter K, a lower value will lower the accuracy of this RC.
SWOT Analysis Untuk Pengembangan Strategy Program Studi Menuju Kelas Dunia Aziz, Okta Qomaruddin; Fatchurrohman, Fatchurrohman; Wahyu Prakasa, Johan Ericka; S, Puspa Miladin; Qosim, Ahmad Latif; Atmalia, Citra Fidya; Crysdian, Cahyo
MATICS Vol 13, No 1 (2021): MATICS
Publisher : Department of Informatics Engineering

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18860/mat.v13i1.10896

Abstract

Teknologi informasi merupakan salah satu bidang yang berkembang pesat di zaman sekarang. Fenomena ini diperkirakan akan terus berlangsung dalam jangka yang sangat panjang. Kualitas program studi tentunya harus mengikuti perkembangan teknologi informasi tersebut. Oleh karena itu perlu dilakukan pendekatan yang komprehensif dalam mengembangkan program studi berdasarkan kondisi yang ada pada program studi tersebut. Penelitian ini akan membahas strategi pengembangan program studi berdasarkan analisis SWOT dan ranking prioritas pada jurusan TI UIN Maulana Malik Ibrahim Malang.
Identifikasi Pohon Tropis di Daerah Perkotaan Menggunakan Multispectral Drone Imagery Zainal Abidin; Fatchurrohman Fatchurrohman; Okta Qomaruddin Aziz
Techno.Com Vol 21, No 4 (2022): November 2022
Publisher : LPPM Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33633/tc.v21i4.6778

Abstract

Vegetasi di daerah perkotaan tumbuh diantara gedung dan jalan. Bangunan di sekitar pohon berdampak pada identifikasi tanaman menggunakan citra drone. Paper ini menjelaskan tentang perbandingan kemampuan citra drone pada band cahaya tampak, near infrared, dan red-edge untuk identifikasi pohon tropis di daerah perkotaan. Kami menyusun dataset yang berisi paduan pohon saman (Samanea Saman), cemara (Casuarina equisetifolia), dan pohon-lain. Setiap citra melalui tahapan proses filtering, segmentasi, dan classification. Hasil uji coba menunjukkan bahwa band cahaya tampak dapat mengidentifikasi saman, cemara, dan pohon-lain dibandingkan dengan citra pada band near-infrared dan red-edge.
Aspect-based Multilabel Classification of E-commerce Reviews using Fine-tuned IndoBERT Ihtada, Fahrendra Khoirul; Alfianita, Rizha; Aziz, Okta Qomaruddin
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 10, No. 1, February 2025
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/kinetik.v10i1.2088

Abstract

In recent years, e-commerce has experienced rapid growth. A significant change in consumer behavior is marked by the ease of access and time flexibility offered by e-commerce platforms, as well as the existence of the review feature to assess products and services. However, with the ever-increasing number of reviews, consumers and store owners face challenges in sorting out relevant information. This research focuses on the multilabel classification of Indonesian e-commerce reviews. This research was undertaken because the application of multilabel classification, especially for e-commerce reviews in Indonesia, has received little attention. This research compares three classification models: end-to-end IndoBERT, IndoBERT-CNN, and IndoBERT-LSTM, to determine the most effective model for multilabel aspect classification of customer reviews. The multilabel classification method was applied to determine the aspect categories of the reviews, such as product, customer service, and delivery, using different thresholds for evaluation. Results show that 0.6 threshold is optimal, with the IndoBERT-LSTM model as the best-performing model for the multilabel aspect classification of these e-commerce reviews. Optimal classification of the model enables more precise information extraction from customer reviews. This can be useful for e-commerce businesses to gain insight from the reviews they get from customers. This insight can be used to find out which aspects need to be improved from the e-commerce business which leads to increased customer satisfaction and trust.
Klasifikasi Penyakit pada Tanaman Berdasarkan Citra Daun Menggunakan Metode Convolutional Neural Network Pangestu, Denis Aji; Aziz, Okta Qomaruddin; Crysdian, Cahyo
JISKA (Jurnal Informatika Sunan Kalijaga) Vol. 10 No. 2 (2025): May 2025
Publisher : UIN Sunan Kalijaga Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14421/jiska.2025.10.2.235-248

Abstract

The agricultural sector is a vital part of the economy, providing food, raw materials, and employment opportunities. In Indonesia, this sector faces significant challenges, such as low interest from younger generations and plant disease issues. Plant disease identification typically requires the expertise of experienced professionals, but this process is time-consuming and costly. This research aims to develop a plant disease classification model using a Convolutional Neural Network (CNN) to assist farmers in identifying diseases in rice, corn, tomato, and potato plants based on leaf images. Testing was conducted with data splitting ratios of 70:30, 80:20, and 90:10, using both single-stage and multi-stage classification methods. The best results were achieved with an 80:20 data ratio using single-stage classification, with an average accuracy of 80%, precision of 80%, recall of 81%, and F1-score of 79%. This study demonstrates that the CNN method is effective in plant disease classification, achieving optimal performance at a 80:20 data ratio and in single-stage classification. It is hoped that this research can help farmers quickly and accurately identify and manage plant diseases, as well as encourage innovation in the agricultural sector. The implementation of CNN in plant disease classification shows great potential in enhancing the efficiency and accuracy of disease detection, ultimately supporting the sustainability and development of the agricultural sector.
Evaluation of Lexical and Semantic Representations in LexRank for Extractive Summarization of Indonesian Friday Sermon Texts Rusydiyyah, Taqiyyah Daaniys Shabrina; Supriyono, Supriyono; Aziz, Okta Qomaruddin; Ummah, Sofwatul; Rahmiasari, Siti Annisa
ILKOMNIKA Vol 8 No 2 (2026): Volume 8, Number 2, August 2026
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28926/ilkomnika.v8i2.933

Abstract

Friday sermon transcripts from YouTube tend to be lengthy, repetitive, and inconsistently punctuated, creating a need for traceable extractive summarization that preserves source sentences. This study compares lexical TF-IDF representation and IndoBERT sentence embeddings in a continuous weighted LexRank architecture using 27 test documents separated at the document level from an initial corpus of 210 transcripts; 32 single-segment transcripts were excluded, leaving 178 eligible documents containing 16,131 sentences. TF-IDF LexRank, Semantic LexRank, and Lead-30% select 30% of the sentences from each document, and their outputs are compared with author-prepared and reviewed extractive reference summaries using ROUGE-1, ROUGE-2, ROUGE-L, 5,000 bootstrap iterations, paired Wilcoxon tests, and Holm correction. TF-IDF LexRank achieves the highest scores of 0.6633 on ROUGE-1, 0.5640 on ROUGE-2, and 0.5881 on ROUGE-L, significantly outperforming Semantic LexRank and Lead-30%. The findings indicate that, under this corpus and evaluation protocol, unigram-bigram lexical representation is better aligned with thematic term repetition and the extractive reference summaries than the tested semantic representation, while semantic representation still outperforms the positional baseline but does not achieve the best performance.
Multi-Class Humor Level Classification of Indonesian Stand-Up Comedy Transcripts Using Fine-Tuned IndoBERT Najib, Jihan; Supriyono, Supriyono; Aziz, Okta Qomaruddin; Andika, Daffa; Davissyah, Asfa
ILKOMNIKA Vol 8 No 2 (2026): Volume 8, Number 2, August 2026
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28926/ilkomnika.v8i2.935

Abstract

Stand-up comedy has become a popular form of entertainment in Indonesia, but humor assessment remains subjective and is generally performed manually. This study develops an automatic classification model for Indonesian stand-up comedy humor levels using transcripts and the IndoBERT language model. The dataset was collected from stand-up comedy videos on the Kompas TV YouTube channel and used audience laughter counts as a pragmatic indicator of humor response. After removing records without transcripts and duplicate transcripts, 2,774 independent records were obtained and categorized into four humor levels: Not Funny, Slightly Funny, Funny, and Very Funny. The dataset was divided into training, validation, and test sets using an 80:10:10 stratified split. IndoBERT was fine-tuned and compared with a majority-class classifier and TF-IDF-based conventional baselines. On the test set, IndoBERT achieved 67.99% accuracy, 67.83% weighted precision, 67.99% weighted recall, and 67.19% weighted F1-score, outperforming the strongest conventional baseline by 11.87 percentage points. Cohen’s Kappa values were 0.5647 unweighted and 0.8309 with quadratic weighting. Moreover, 91.0% of misclassifications occurred in adjacent categories, indicating that the model captures the ordinal structure of humor levels. These results demonstrate that IndoBERT provides a viable baseline for Indonesian humor-level classification, although distinguishing between adjacent humor categories remains challenging.
Co-Authors Alfianita, Rizha Andika, Daffa Atmalia, Citra Fidya Cahyo Crysdian Davissyah, Asfa Fatchur Rohman Fatchurrohman Fatchurrohman, Fatchurrohman Ihtada, Fahrendra Khoirul Johan Ericka Wahyu Prakasa Najib, Jihan Pangestu, Denis Aji Qosim, Ahmad Latif Rahmiasari, Siti Annisa Rusydiyyah, Taqiyyah Daaniys Shabrina S, Puspa Miladin Supriyono Supriyono Ummah, Sofwatul Zainal Abidin