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All Journal JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) bit-Tech Jurnal Teknologi Informatika dan Komputer JUSTER: Jurnal Sains dan Terapan Jurnal Komputer Teknologi Informasi Sistem Komputer (JUKTISI) Journal of Artificial Intelligence and Technology Information Horizon: Indonesian Journal of Multidisciplinary Smatika Jurnal : STIKI Informatika Jurnal Journal of Education and ICT
Agung Prasetya
Universitas Bhinneka PGRI
Agriculture, Biological Sciences & Forestry Humanities Biochemistry, Genetics & Molecular Biology Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Immunology & microbiology Languange, Linguistic, Communication & Media Physics Social Sciences Other

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Usulan Desain User interface Website Perguruan Tinggi Berdasarkan Aspek Kognitif dan Afektif Pengguna Taufiq Agung Cahyono; Agung Prasetya
bit-Tech Vol. 7 No. 2 (2024): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v7i2.1933

Abstract

Dengan pesatnya perkembangan teknologi informasi merubah banyak sudut pandang dari berbagai unsur, tidak terkecuali lembaga perguruan tinggi dalam berinteraksi dengan berbagai pemangku kepentingan. Pemanfaatan teknologi digital, khusunya website telah menjadi aspek penting dalam memperkuat citra perguruan tinggi dan berinteraksi dengan masyarakat luas. Website perguruan tinggi saat ini menjadi titik kontak pertama kali bagi calon mahasiswa, orangtua, alumni dan pemangku kepentingan lainya. Namun ada kalanya tampilan user interface tidak sepenuhnya memenuhi ekspektasi pengguna. Salah satu penyebanya adalah tidak sesuainya desain user interface tersebut dengan kemampuan ergonomi pengguna. Dapat disimpulkan bahwa desain user interface website perguruan tinggi yang efektif, bukan hanya soal estetika namun desain user interface juga harus memperhitungkan kemampuan kognitif dan afektif sehingga memenuhi usability yang memudahkan pengguna dalam menemukan informasi yang mereka butuhkan. Tujuan penelitian ini adalah mencari pengaruh  peran komponen desain terhadap aspek kognitif, afektif dan sikap pengguna. Serta memberikan usulan desain user interface website untuk perguruan tinggi yang mempertimbangkan kemampuan ergonomi manusia. Metode kuantitatif merupakan metode yang digunakan dalam penelitian ini. Metode dilakukan dengan cara menyebar kuisioner kepada responden kemudian hasil kuisioner akan diolah menggunakan aplikasi smartPLS 4 untuk mengetahui hubungan antar variabel penelitian. Temuan dari penelitian ini adalah komponen desain font dan layout memberikan pengaruh signifikan terhadap hubungan kognitif dengan sikap. Sedangkan komponen desain warna memberikan pengaruh signifikan terhadap hubungan kognitif dengan sikap dan hubungan dengan afektif dengan sikap. Dari penelitian ini juga didapatkan bahwa font dengan jenis roboto, layout dengan jenis modular dan warna background biru menjadi yang paling dominan dipilih oleh responden.
AN SVM-BASED APPROACH FOR DETECTING DATA DEFINITION LANGUAGE OPERATIONS IN INDONESIAN NATURAL LANGUAGE Yayak Kartika Sari; Fahrur Rozi; Agung Prasetya
JoEICT (Jurnal of Education And ICT) Vol 8, No 2 (2024)
Publisher : STKIP PGRI TULUNGAGUNG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29100/joeict.v8i2.1649

Abstract

Text-to-SQL is an approach that enables users to interact with data-bases using natural language, eliminating the need to understand SQL syntax. However, most existing approaches translate input sentences directly into final SQL queries without explicitly identifying the type of SQL operation involved. This may obscure the distinction between structural and manipulative commands and increase the risk of execut-ing unintended or destructive queries. This study proposes separating the identification of SQL operation types—specifically Data Definition Language (DDL) commands—as a standalone classification task using the Support Vector Machine (SVM) algorithm. Indonesian-language sentences are preprocessed through tokenization, stopword removal, and stemming, then transformed into feature vectors using TF-IDF with unigram and bigram representations. Experiments were conducted on a dataset of 800 Indonesian sentences covering four DDL operations: CREATE, ALTER, DROP, and TRUNCATE. The results show that the proposed SVM model achieved an average accuracy of 93.05%, out-performing baseline models such as Naive Bayes and Random Forest. These findings indicate that early identification of SQL operation types can enhance the accuracy, efficiency, and safety of Text-to-SQL sys-tems. This work also highlights the importance of developing NLP ap-proaches tailored for the Indonesian language in the context of data-base querying.
IDENTIFYING ARITHMETIC OPERATION IN MATH WORD PROBLEM BASED ON RECURSIVE NEURAL NETWORK AND SUPPORT VECTOR MACHINE Agung Prasetya
JoEICT (Jurnal of Education And ICT) Vol 8, No 2 (2024)
Publisher : STKIP PGRI TULUNGAGUNG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29100/joeict.v8i2.7421

Abstract

Math word problems act as a test bed to design an intelligent system. An approach is needed to identify arithmetic operations including addition, substraction, multiplication and division. Template-based approaches have addressed this problem. However, the template-based approach is less efficient because it requires the process of building a template repository that have to cover a wide variety of story implied by math words. The template-based approach is potentially sub-optimal when solving story problems that have not been covered yet by templates. The proposed approach resolves this by using Recursive Neural Network and Support Vector Machine. Recursive neural network is used as an encoder that can generate semantic vectors of math word problems. Then, this vector becomes as an input for a Support Vector Machine-based classifier. Tests were conducted on a dataset collected manually from Kemdikbud’ electronic school books. The results showed that the proposed approach does not require the formation of templates, thereby reducing human involvement. In addition, the use of Recursive Neural Network reduces feature engineering making it more efficient. Experimental results by applying k-fold cross validation show that the proposed approach has an accuracy of 81% and a precision of 66%
ANALISA METODE FUZZY C MEANS UNTUK KLASTERISASI KINERJA TEKNISI CCAN TELKOM KEDIRI Yayak Kartika Sari; Joko Iskandar; Agung Prasetya
JoEICT (Jurnal of Education And ICT) Vol 6, No 1 (2022)
Publisher : STKIP PGRI TULUNGAGUNG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29100/joeict.v6i1.1208

Abstract

PT. Telkom akses membutuhkan sumber daya manusia (SDM) yang berkualitas dan berkompetensi baik dalam aspek skill maupun aspek knowledge. Dalam menciptakan sumber daya manusia yang berkualitas diperlukan penilaian pekerjaan para teknisi khususnya pada divisi CCNA PT. Telkom akses Kediri. Penilaian tersebut penting dilakukan karena berkaitan dengan prestasi yang dicapai oleh setiap teknisi. Penilaian tersebut dilakukan dengan memberikan nilai terhadap setiap teknisi berdasarkan kriteria yang telah ditetapkan oleh perusahaan menggunakan sistem manual, sehingga suatu manager membutuhkan waktu yang lama dalam melakukan penilaian karena berkaitan dengan jumlah teknisi PT. TELKOM Akses di Kediri relative banyak sebesar 37 teknisi selain itu juga setiap karyawan memiliki kompetensi yang hampir sama. Maka dari itu perlunya Klasterisasi karyawan PT. Telkom akses agar memudahkan manager dalam mengambil sebuah keputusan. Pada penelitian ini menggunakan Fuzzy C-Means dalam teknik klasterisasi. Dari data teknisi diproses menggunakan metode fuzzy c means dengan pendefinisian parameter awal yaitu max iter sebesar 100, bobot sebesar 2, target error sebesar 0,001 dan fungsi objektif awal yaitu 0, dan diperoleh cluster 1 sebanyak 8 karyawan, cluster 2 sebanyak 22 karyawan, dan cluster 3 sebanyak 7 karyawan.
Structural Classification of Indonesian Arithmetic Word Problems Using Hierarchical Agglomerative Clustering Mochammad Dhani Aprianto; Agung Prasetya
Jurnal Komputer Teknologi Informasi Sistem Komputer (JUKTISI) Vol. 5 No. 1 (2026): Juni 2026
Publisher : LKP KARYA PRIMA KURSUS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62712/juktisi.v5i1.1026

Abstract

Arithmetic word problems (MWP) are a fundamental component of elementary mathematics education that integrate linguistic comprehension with quantitative reasoning. In practice, collections of MWPs are commonly organized based on teacher intuition or broad curriculum categories, which are inherently subjective and often fail to reflect the true mathematical similarity between problems. This study aims to classify Indonesian arithmetic word problems based on their underlying relational structures using Hierarchical Agglomerative Clustering (HAC). The dataset consists of 897 elementary-level arithmetic word problems represented through 143 binary features encoding five relational dimensions, namely combine, change, compare, equal groups, and fair division. Hamming Distance is employed as the dissimilarity metric, and clustering is performed using the complete linkage method. The optimal number of clusters is determined using three internal validity indices: the Calinski–Harabasz Index, Silhouette Score, and Davies–Bouldin Index. Although statistical indices favor smaller cluster configurations, four clusters are selected as the optimal number based on domain-specific interpretability, as they align with established theoretical categories of arithmetic relational structures. This approach effectively identifies latent structural patterns within the dataset and demonstrates the potential of feature-based binary representation combined with HAC for systematic MWP classification. The findings offer practical support for adaptive problem bank development, automated curriculum analysis, and intelligent tutoring system design.
Penerapan Metode Naive Bayes Dalam Analisis Sentimen Terhadap Cyberbullying Yayak Kartika Sari; Joko Iskandar; Agung Prasetya
JUSTER : Jurnal Sains dan Terapan Vol. 5 No. 1 (2026): JUSTER: Jurnal Sains dan Terapan
Publisher : Jompa Research and Development

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57218/juster.v5i1.2556

Abstract

Kasus bullying menjadi topik yang sangat ramai dibicarakan tidak hanya pada lingkup daerah tetapi kasus ini menjadi topik yang ramai di lingkup Nasional maupun Internasional. Seiring dengan perkembangan teknologi, kasus bullying ini tidak hanya terjadi secara langsung tetapi melalui media sosial. Pelaku bullying dapat dengan mudah menyebarkan foto, video, maupun tulisan yang dapat menyinggung korban. Dampak yang terjadi pada korban cyberbullying tidak hanya menyakiti perasaan, namun juga kondisi psikologis yang dapat menyebabkan depresi, sedih, frustasi, hingga bunuh diri. Kasus cyberbullying ini banyak mendapatkan respon dari masyarakat melalui media sosial terutama pada aplikasi X. Penulis bertujuan untuk melakukan analisis sentimen yang digunakan untuk menganalisis emosi dari suatu teks dalam Bahasa Indonesia apakah teks tersebut termasuk dalam kategori positif atau negatif, dalam hal ini sentimen analisis digunakan untuk menemukan pola-pola cyberbullying di aplikasi X agar dapat lebih tepat dan cepat dalam mendeteksi bullying secara otomatis dengan menggunakan metode Naïve Bayes dengan jumlah data_set sebesar 2292 dari rentang Bulan Agustus 2024 – Agustus 2025. Metode Naive Bayes yaitu memprediksi probabilitas, tetapi klasifikasi statistik yang dikenal sebagai klasifikasi Bayes dapat memprediksi nilai probabilitas. Hasil dari evaluasi sentiment Analisa menunjukkan bahwa accuracy sebesar 96,0%, precision 84%, dan recall 77%.
Identification of Quantity Relationships in Math Story Problems Using Bidirectional Long Short-Term Memory (Bi-LSTM) Diana Ayu Puspita; Agung Prasetya; Yayak Kartika Sari
HORIZON: Indonesian Journal of Multidisciplinary Vol. 4 No. 3 (2026): HORIZON: Indonesian Journal of Multidisciplinary
Publisher : Lembaga Intelektual Muda (LIM) Maluku

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54373/hijm.v4i3.6049

Abstract

Penelitian ini bertujuan untuk mengidentifikasi hubungan kuantitas dalam soal cerita matematika menggunakan metode Bidirectional Long Short-Term Memory (BiLSTM). Permasalahan dalam penelitian ini adalah kesulitan dalam memahami dan mengklasifikasikan hubungan kuantitas secara otomatis akibat variasi struktur kalimat dan konteks bahasa. Metode yang digunakan adalah pendekatan deep learning dengan tahapan pengumpulan data, preprocessing, pembentukan vocabulary, pelatihan model, dan evaluasi model. Hasil penelitian menunjukkan bahwa model BiLSTM mampu mengklasifikasikan hubungan kuantitas dengan baik dengan memperoleh nilai accuracy sebesar 0.9224 serta nilai precision, recall, dan F1-score yang relatif tinggi pada setiap kelas. Hal ini menunjukkan bahwa model memiliki performa yang baik dalam memahami pola hubungan kuantitas dalam teks soal cerita matematika, sehingga dapat digunakan sebagai pendekatan yang efektif untuk identifikasi hubungan kuantitas secara otomatis.
ANALISIS SENTIMEN PADA TREN OPINI PUBLIK TERHADAP PROGRAM #MAKANBERGIZIGRATIS DI PLATFORM X MENGGUNAKAN JARINGAN LONG SHORT-TERM MEMORY (LSTM) Veny Dwi Wahyuningsih; Yayak Kartika Sari; Agung Prasetya
SMATIKA JURNAL : STIKI Informatika Jurnal Vol 16 No 01 (2026): SMATIKA Jurnal : STIKI Informatika Jurnal
Publisher : LPPM Universitas Bhinneka Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32664/smatika.v16i01.2260

Abstract

The implementation of the Free Nutritious Meal Program (Makan Bergizi Gratis/MBG) as a strategic government initiative has generated diverse responses from the public, widely discussed on social media, particularly on the X (Twitter) platform. Differences in perceptions regarding the objectives, implementation, and impacts of the policy have encouraged intensive public discussions. However, the tendency of public sentiment toward this program has not been widely analyzed systematically using machine learning approaches based on contextual representations. Therefore, this study analyzes public sentiment toward the hashtag #MakanBergiziGratis using the Long Short-Term Memory (LSTM) method. A total of 5,516 Indonesian-language tweets were collected through a web scraping process within the period of January 1 to November 30, 2025. Sentiment labeling employed a lexicon-based approach to classify the data into three categories: positive, neutral, and negative. The analysis stages included text preprocessing, BERT tokenization and embedding, handling imbalanced data using the Synthetic Minority Over-sampling Technique (SMOTE), and sentiment classification using LSTM. The results reveal that neutral sentiment dominates with 60.80%, followed by positive sentiment at 34.34% and negative sentiment at 4.86%. The developed model achieved an accuracy of 82.50% with a weighted F1-score of 82.66%. Furthermore, evaluation using 5-fold cross-validation produced an average accuracy of 82.8%, indicating stable model performance and good generalization capability in identifying public opinion trends toward the MBG policy.
Multi-Sentence Contextual Modeling using Transformer Architecture for Arithmetic Operation Identification in Mathematical Word Problems Cikal Dwidyanto; Agung Prasetya; Mohamad Khoirul Ansor
Jurnal Teknologi Informatika dan Komputer Vol. 12 No. 1 (2026): Jurnal Teknologi Informatika dan Komputer
Publisher : Universitas Mohammad Husni Thamrin

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37012/jtik.v12i1.3431

Abstract

Mathematical word problems in Indonesian are generally presented as multi-sentence paragraphs, making the identification of arithmetic operations not solely dependent on recognizing numerical values, but also requiring an understanding of events and semantic relationships across sentences. This study formulates the task of arithmetic operation identification as a classification problem of a single primary operation into four classes: addition, subtraction, multiplication, and division. To capture contextual relationships across sentences, an encoder-based Transformer architecture is employed, which is capable of modeling long-range dependencies through a self-attention mechanism. The dataset consists of 900 elementary school-level mathematical word problems constructed in accordance with the Indonesian curriculum. Experimental results show that the model achieves an accuracy of 0.98 and an F1-score of 0.98. Per-class evaluation indicates high and consistent performance, although prediction errors are still observed in cases with ambiguous narrative patterns, particularly where addition is misclassified as multiplication or subtraction, and multiplication is misclassified as division. These findings demonstrate that the Transformer architecture is effective in leveraging multi-sentence context to improve the accuracy of arithmetic operation identification in mathematical word problems.
THE APPLICATION OF THE MULTI-OBJECTIVE OPTIMIZATION ON THE BASIS OF SIMPLE RATIO ANALYSIS METHOD IN A DECISION SUPPORT SYSTEM FOR PROSPECTIVE UBT STUDENT ASSOCIATION CHAIR CANDIDATES Awang Pradana; Arif Fadllullah; Agung Prasetya; Fadliansyah Fadliansyah
JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Vol 10, No 2 (2025)
Publisher : STKIP PGRI Tulungagung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29100/jipi.v10i2.8227

Abstract

Decision Support Systems (DSS) have become essential tools in the de-cision-making process across various fields. In the context of selecting the chairman of the Computer Engineering Student Association at Uni-versitas Borneo Tarakan, the use of DSS is also highly relevant and beneficial. The MOOSRA (Multi-Objective Optimization on the basis of Ratio Analysis) method has been chosen as the approach to implement this decision support system. This study aims to apply the MOOSRA method in the implementation of a web-based decision support system for the selection of prospective chairpersons of the Computer Engineer-ing Student Association at Universitas Borneo Tarakan. The MOOSRA method is utilized to consider several criteria, such as leadership skills, communication abilities, dedication, and organizational experience. In this research, the use of MOOSRA is combined with web technology to enhance the efficiency and quality of the candidate selection process. The MOOSRA method offers a structured and objective approach to evaluating candidates for the chairmanship. This approach involves ratio analysis and multi-objective optimization to produce better out-comes. The results of this study are expected to facilitate a fairer and more objective selection process, as well as to improve student satisfac-tion within the Computer Engineering Student Association at Universi-tas Borneo Tarakan.
Co-Authors Arif Fadllullah Awang Pradana, Awang Cikal Dwidyanto Diana Ayu Puspita Fadliansyah Fadliansyah Fahrur Rozi Feri Andriawan Saputra Ferri Nurdiansah Iskandar, Joko Mochammad Dhani Aprianto Mohamad Khoirul Ansor Sari, Yayak Kartika Taufiq Agung Cahyono Veny Dwi Wahyuningsih