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All Journal Building of Informatics, Technology and Science JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Infotech: Journal of Technology Information Jurnal Teknik Informatika (JUTIF) Jurnal Ilmu Komputer
Waskita, Arya Adhyaksa
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management

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KLASIFIKASI STATUS GIZI BALITA MENGGUNAKAN ALGORITMA K- NEAREST NEIGHBOR DAN NAÏVE BAYES (STUDY KASUS: UPTD PUSKESMAS BAMBU APUS) Ghora Pangumbara’an, Mutiara Sukma; Waskita, Arya Adhyaksa; Makshun, Makshun
Infotech: Journal of Technology Information Vol 10, No 2 (2024): NOVEMBER
Publisher : ISTEK WIDURI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37365/jti.v10i2.305

Abstract

The nutritional status of toddlers is influenced by various factors, both direct, such as infectious diseases, birth history, exclusive breastfeeding, and the quality and quantity of food, as well as indirect factors, including socio-economic status, education, knowledge, and healthcare behavior. Exclusive breastfeeding is particularly crucial as it provides the most complete source of nutrients and is essential for proper growth and development (both brain and body). These factors play a significant role in determining the physical and mental development of children. Malnutrition in toddlers can lead to serious consequences, including physical growth disorders, delayed mental development, and increased risk of disease. Therefore, proper nutrition is essential, especially during early childhood, when nutritional needs are higher than in other age groups. In this context, the study developed a classification model for toddler nutritional status using the K-Nearest Neighbor (KNN) and Naïve Bayes algorithms, comparing the accuracy of both algorithms. The study data was collected from Bambu Apus Community Health Center, involving 424 toddlers aged 0-60 months, with nutritional status assessment criteria based on age, weight, and height or length, which were converted into Z-scores according to the WHO 2005 anthropometric standards. Data testing and analysis were conducted using the RapidMiner 10.3 application. The results showed that the majority of toddlers in the area had good nutritional status, with the highest accuracy achieved by the Naïve Bayes algorithm at 87.65% using a 60:40 hold-out method. This study provides valuable insights into the prevalence of nutritional status at Bambu Apus Community Health Center and emphasizes the importance of regular monitoring of toddler nutritional status. Additionally, the study contributes to identifying effective classification methods for toddler nutritional status, which can support more targeted nutrition intervention programs. ABSTRAKStatus gizi balita dipengaruhi oleh berbagai faktor, baik langsung seperti penyakit infeksi, riwayat lahir, pemberian ASI Eksklusif, serta mutu dan kuantitas makanan, maupun tidak langsung seperti sosial ekonomi, pendidikan, pengetahuan, dan perilaku terhadap layanan kesehatan. Riwayat Pemberian ASI Eksklusif merupakan hal yang mempengaruhi status gizi karena ASI miliki sumber zat gizi yang paling lengkap, dan harus diberikan kepada anak, sehingga pertumbuhan dan perkembangan (otak dan tubuh) baik. Faktor-faktor ini memegang peran penting dalam menentukan perkembangan fisik dan mental anak. Kekurangan gizi pada balita dapat menimbulkan dampak serius, termasuk gangguan pertumbuhan fisik, keterlambatan perkembangan mental, dan peningkatan risiko penyakit. Oleh karena itu, pemenuhan gizi yang tepat sangat penting, terutama pada masa balita yang membutuhkan asupan gizi lebih besar dibandingkan kelompok usia lainnya. Dalam hal ini, penelitian mengembangkan model klasifikasi status gizi balita menggunakan algoritma K-Nearest Neighbor (KNN) dan Naïve Bayes, serta membandingkan akurasi kedua algoritma tersebut dengan data testing – training 60:40, 70:30 dan 80:20. Data penelitian diambil dari Puskesmas Bambu Apus, melibatkan 424 balita usia 0-60 bulan, dengan kriteria penilaian status gizi berdasarkan usia, berat badan, serta tinggi atau panjang badan yang dikonversi ke dalam nilai Z-score sesuai standar antropometri WHO 2005. Pengujian dan analisis data dilakukan menggunakan aplikasi RapidMiner 10.3. Hasil penelitian menunjukkan bahwa mayoritas balita di wilayah tersebut memiliki status gizi baik, dengan akurasi tertinggi pada algoritma Naïve Bayes sebesar 87,65% menggunakan metode hold-out 60:40. Penelitian ini memberikan wawasan penting mengenai prevalensi status gizi di Puskesmas Bambu Apus dan menekankan pentingnya pemantauan status gizi balita. Selain itu, penelitian ini juga berkontribusi dalam mengidentifikasi metode klasifikasi yang efektif untuk status gizi balita, yang dapat mendukung program intervensi gizi yang lebih tepat sasaran.
Optimasi Akurasi Jawaban Aplikasi Chatbot Layanan Pelanggan dengan Metode RAGRetrieval-Augmented Generation Dhaman, Dhaman; Anggai, Sajarwo; Waskita, Arya Adhyaksa
Journal of Information System Research (JOSH) Vol 6 No 4 (2025): Juli 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v6i4.8048

Abstract

This research addresses the issue of low answer accuracy in chatbot systems based on Large Language Models (LLMs) when responding to questions derived from customer service documents. To overcome this problem, the Retrieval-Augmented Generation (RAG) method is applied to improve the quality of responses by adding relevant context from external documents. Three LLM models used in this study are LLaMA3.1 8B, LLaMA3.2 1B, and LLaMA3.2 3B from Meta AI. Evaluation is conducted using automatic ROUGE metrics (ROUGE-1, ROUGE-2, and ROUGE-L) and manual human evaluation assessing accuracy, relevance, and hallucination. This research contributes to the development of more reliable question-answering systems based on LLMs enhanced with external contextual documents related to customer service information. The results show a significant improvement across all models after applying the RAG method. ROUGE F1-scores increased consistently, with Llama3.1:8b showing the highest gain (from 0.12 to 0.58 on ROUGE-1). Human evaluation also confirmed improvements in accuracy (up to +2.73 points) and reductions in hallucination (up to −2.63 points). These improvements were evident not only in larger models but also in smaller ones, indicating that the benefits of RAG are not dependent on model size. In conclusion, RAG is highly effective in enhancing the accuracy and reliability of chatbot responses, especially in document-based question-answering scenarios. By leveraging contextual information from external documents, the system produces more factual, relevant, and hallucination-free responses. RAG has proven to be an effective approach for enhancing the response quality of LLM, including those with smaller parameter sizes.
Enhancing BERTopic with Neural Network Clustering for Thematic Analysis of U.S. Presidential Speeches Anggai, Sajarwo; Zain, Rafi Mahmud; Tukiyat, Tukiyat; Waskita, Arya Adhyaksa
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 4 (2025): JUTIF Volume 6, Number 4, Agustus 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.4.5090

Abstract

Understanding the underlying themes in presidential speeches is critical for analyzing political discourse and determining public policy direction.  However, topic modeling in this context presents difficulties, particularly when clustering semantically rich topics from high-dimensional embeddings.  This study seeks to improve topic modeling performance by incorporating a Neural Network Clustering (NNC) approach into the BERTopic pipeline.  We analyze 2,747 speeches delivered by U.S President Joe Biden (2021-2025) and compare three clustering techniques: HDBSCAN, KMeans, and the proposed Autoencoder-based NNC.  The evaluation metrics (UMass, NPMI, Topic Diversity) show that NNC produces the most coherent and diverse topic clusters (UMass = -0.4548, NPMI = 0.0234, Diversity = 0.3950, ).  These findings show that NNC can overcome the limitations of density and centroid-based clustering in high-dimensional semantic spaces. The study contributes to the field of Natural Language Processing by demonstrating how neural-based clustering can improve topic modeling, particularly for complex, real-world political corpora.
Narasi Presiden Indonesia: Analisis Wacana Politik Menggunakan BERTopic dalam Mengungkap Pola Tematik Pidato Presiden Uliyatunisa, Uliyatunisa; Tukiyat, Tukiyat; Waskita, Arya Adhyaksa; Handayani, Murni; Zain, Rafi Mahmud
Building of Informatics, Technology and Science (BITS) Vol 7 No 2 (2025): September 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i2.8298

Abstract

The speeches of the President of Indonesia play an important role as a means of political communication, policy delivery, and leadership image building in front of the public. However, the increasing volume of speeches presents new challenges in the manual analysis process, as it is time-consuming and prone to researcher subjectivity. This study offers a solution by using BERTopic, a transformer-based topic modelling method that utilises semantic representations from modern embedding models. The research data consists of transcripts of President Joko Widodo's official speeches obtained from the Cabinet Secretariat portal. To improve the quality of semantic representations, this study compares several Indonesian language embedding models, namely DistilBERT, NusaBERT, IndoE5, and SBERT. The analysis process was carried out through the stages of data preprocessing, embedding formation, dimension reduction, clustering, and model evaluation using topic coherence metrics. The objectives of this study were to reveal the themes contained in the President's speeches and to evaluate the effectiveness of embedding models in producing more coherent topics. The results show twenty main themes that consistently appear, including infrastructure development, economic policy, health and the pandemic, digital transformation, international diplomacy, sports, nationalism issues, and regional development. In terms of performance, SBERT provides the best results with a coherence value of UMass = -2.036 and NPMI = 0.082, indicating a positive semantic relationship. A UMass value close to zero indicates greater coherence of words within a topic, while an NPMI value above zero indicates that the connections between words are more easily understood by humans. This research contributes to the development of NLP-based political discourse studies in Indonesia, providing an empirical overview of the selection of appropriate embedding models in topic modelling and opening up opportunities for the integration of similar methods in public policy analysis.
Analisis Manajemen Risiko Framework COBIT 2019 Dengan Metode AHP (Studi Kasus: PT Apro Global Solusi) Rohman, Fredi Muhammad; Susanto, Agung Budi; Waskita, Arya Adhyaksa
Jurnal Ilmu Komputer Vol 1 No 1 (2023): Jurnal Ilmu Komputer (Edisi Juni 2023)
Publisher : Universitas Pamulang

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

Abstract

PT Apro Global Solusi (AGS) is an information technology company that helps customers achieve their business goals by providing the best consulting in its class. Problems that occur at PT AGS such as data leaks, suboptimal governance make it a reproach for the occurrence of risks in the IT division, This research will use the Cobit 2019 framework and the AHP (Analytic Hierarchy Process) method to make decisions. The research object will focus on risk management in PT AGS IT division so that it has good control in IT risk management. Cobit 2019 implementation and the AHP method can be implemented properly so that AGS can set standards for good governance and minimize risks that will occur, with decision makers the AHP method can be a reference for the IT Division to look for flaws that can cause risks and create IT divisions PT Apro Global Solusi has good standards.
Co-Authors Agung Budi Susanto Anggai, Sajarwo Dhaman, Dhaman Ghora Pangumbara’an, Mutiara Sukma Makshun, Makshun Murni Handayani Rohman, Fredi Muhammad Tukiyat Tukiyat, Tukiyat Uliyatunisa, Uliyatunisa Zain, Rafi Mahmud