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ILKOMNIKA: Journal of Computer Science and Applied Informatics
Published by Universitas Nahdlatul Ulama Blitar
ISSN : -     EISSN : 27152731     DOI : https://doi.org/10.28926/ilkomnika
Core Subject : Science, Engineering,
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management
ILKOMNIKA: Journal of Computer and Applied Informatics is is a peer reviewed open-access journal. The journal invites scientists and engineers throughout the world to exchange and disseminate theoretical and practice-oriented topics of computer science and applied informatics which covers five (5) majors areas of research that includes 1) Informatics Engineering and Its Application 2) Computer Science 3) Software Engineering 4) Computer Engineering 5) Information System. This journal is published 3 issues a year, in April, August, and December.
Arjuna Subject : Ilmu Komputer (semua kategori) - Aplikasi Ilmu Komputer
Articles 244 Documents
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Black Box Testing Using Equivalence Partitioning for Indonesian Livestock Population Prediction Application Prabowo, Tito; Lestariningsih, Lestariningsih; Karomah, Siti; Ramadhani, Karunia Putri Anggrai
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.v8i2.932

Abstract

When software is developed, thorough functional validation is essential to ensure that applications operate according to design specifications and user expectations. Inadequate data validation can lead to inaccurate database records, causing prediction models to return unreliable forecasts that negatively affect stakeholders. This paper presents a functional verification of an Indonesian livestock population prediction web platform using the Equivalence Partitioning (EP) technique within a Black Box Testing framework. Rather than focusing solely on output validation, this study formalizes input domains by partitioning them into valid and invalid equivalence classes across five core application modules: Authentication, Population Data Management, Predictive Analytics Execution, News Content Administration, and Interactive Mapping. To evaluate testing quality quantitatively, a Test Effectiveness Metric based on the ratio of successfully executed valid test cases to total designed partitions was introduced, achieving a functional effectiveness score of 100% across all 9 designed test cases. The empirical findings confirm that the system correctly enforces input constraints and error-handling logic, providing a robust web application for agricultural planning.
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.
Evaluating Institutional Readiness for Artificial Intelligence Adoption in Higher Education: A Comprehensive Assessment Framework Husain, Syepry Maulana
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.v8i2.939

Abstract

The rapid proliferation of Artificial Intelligence (AI), particularly Generative AI, has catalyzed digital transformation within higher education, shifting paradigms across instruction, research, and governance. This study evaluates institutional readiness for AI adoption at Muhammadiyah University of Tangerang (UMT) using an adapted, five-dimensional AI Readiness Framework covering Vision and Strategy, Governance and Ethics, Human Resources Capability, Data Management, and Technology Infrastructure. Employing a sequential explanatory mixed-methods design, quantitative data were collected from 125 stakeholders and subsequently explained through interviews and field observations with five key informants. The findings reveal an overall AI Readiness Index (AIRI) of 2.92 out of 5.00, placing the institution in the Awareness/Transition phase. Technology Infrastructure (3.90) and Vision and Strategy (3.25) show the most advanced development, while Governance and Ethics (2.15) and Data Management (2.45) remain the principal constraints. The study’s contribution is an empirically validated institutional-level instrument and assessment procedure rather than a new theoretical framework that measures AI readiness beyond individual or technological readiness alone, and it demonstrates how quantitative indices can be systematically triangulated with qualitative evidence to prioritize institutional interventions.

Page 25 of 25 | Total Record : 244

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