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Toddler Nutritional Status Classification Using C4.5 and Particle Swarm Optimization Nazir, Alwis; Akhyar, Amany; Yusra, Yusra; Budianita, Elvia
Scientific Journal of Informatics Vol 9, No 1 (2022): May 2022
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v9i1.33158

Abstract. Purpose: This research was conducted to create a classification model in the form of the most optimal decision tree. Optimal in this case is the combination of parameters used that will produce the highest accuracy compared to other parameter combinations. From this best model, it will be used to predict the nutritional status class for the new data.Methods/Study design/approach: The dataset used is from Nutritional Status Monitoring in 2017 in Riau Province, Indonesia. From the dataset, the Knowledge Discovery in Database (KDD) stages were carried out to build several classification models in the form of decision trees. The decision tree that has the highest accuracy will then be selected to predict the class for the new data. Predictions for new data (unclassified data) will be made in a web-based system.Result/Findings: Particle Swarm Optimization is used to find optimal parameters. Before PSO is used, there are 213 parameters in the dataset that can be used to do classification. However, using many such parameters is time-consuming. After PSO is used, the optimal parameters found are the combination of 4 parameters, which can produce the most optimal decision tree. The 4 chosen parameters are gender, age (in months), height, and the way to measure the height (either stand up or lie down). The most optimal decision tree has an accuracy of 94.49%. From the most optimal decision tree, a web-based system was built to predict the class for new data (unclassified data).Novelty/Originality/Value: Particle Swarm Optimization (PSO) is a method that can help to select the most optimal parameters, or in other words produce the highest classification accuracy. The combination of parameters selected has also been confirmed by the nutritionist. The prediction system has been declared feasible to be used by nutritionists through the User Acceptance Test (UAT).

Pembentukan Kelompok Mahasiswa/Alumni Content Creator Gizi Seimbang Remaja untuk Mencegah Stunting dalam Aplikasi Stunting Calculator: Aplikasi Stunting Calculator Hayati, Aslis Wirda; Husnan, Husnan; Roziana, Roziana; Pizaini, Pizaini; Akhyar, Amany
Jurnal IDAMAN (Induk Pemberdayaan Masyarakat Pedesaan) Vol. 7 No. 2 (2023): Jurnal IDAMAN (Induk Pemberdayaan Masyarakat Pedesaan)
Publisher : Politeknik Kesehatan Kemenkes malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31290/j.idaman.v7i2.4037

The problem of stunting adolescents in the world is currently 22.2% or about more than 150.8 million. In efforts to prevent and reduce the prevalence of stunting, it is necessary to provide education to adolescents regarding food consumption in accordance with the rules of balanced nutrition. Community service aims to foster new entrepreneurs based on science and technology, teaching skills by incorporating health sciences through digital training. The book "Balanced Nutrition for Adolescents: Prevent Stunting" has been digitally transformed and integrated into a stunting calculator application on smartphones, enabling adolescents to become content creators. Adolescents are expected to have knowledge about nutrition that will be inputted into the stunting calculator application, understand the preparation for data entry, and utilize the digitized book on a smartphone. The training took place from February to March 2023, comprising five Zoom meetings and one in-person session. There were 23 participants in the training, consisting of students and alumni from the Departments of Nutrition, Nursing, Midwifery, Medical Records, and Informatics Engineering in universities located in Riau and West Sumatra provinces. Each meeting involved assigning tasks to the participants, followed by assessment and discussion at the beginning of subsequent sessions. After the training, participants formed WhatsApp groups for communication and discussion to create content based on their respective interests. The training participants were awarded competence certificates as content creators

Analisis Aspek Filsafat Sains pada Bidang Graph Mining dan Kemiripan Aturan Hukum Akhyar, Amany
Jurnal Teknomatika Vol 17 No 1 (2024): TEKNOMATIKA
Publisher : Fakultas Teknik dan Teknologi Informasi, Universitas Jenderal Achmad Yani Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30989/teknomatika.v17i1.1301

Hukum di Indonesia berjumlah sangat banyak dan saling berhubungan atau merujuk antara satu sama lain. Salah satu fenomena hukum yang ada di Indonesia yaitu terdapat kemiripan aturan hukum sehingga menimbulkan masalah yaitu aturan hukum yang tumpang tindih dan tidak sinkron. Ketika aturan hukum yang baru akan dibentuk, sebaiknya tersedia tools yang dapat melakukan pemeriksaan terlebih dulu terhadap hukum lama sehingga tidak tumpang tindih dan sinkron. Knowledge graph (KG) digunakan pada penelitian ini untuk merepresentasikan hukum-hukum yang ada di Indonesia. Penelusuran KG tersebut akan dibantu dengan teknologi graph mining. Pada penelitian ini, dibahas pula aspek-aspek filsafat sains pada beberapa penelitian terdahulu yang berkaitan dengan topik penelitian ini.

Analisis Aspek Filsafat Sains pada Bidang Graph Mining dan Kemiripan Aturan Hukum Akhyar, Amany
Jurnal Teknomatika Vol 17 No 1 (2024): TEKNOMATIKA
Publisher : Fakultas Teknik dan Teknologi Informasi, Universitas Jenderal Achmad Yani Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30989/teknomatika.v17i1.1301

Hukum di Indonesia berjumlah sangat banyak dan saling berhubungan atau merujuk antara satu sama lain. Salah satu fenomena hukum yang ada di Indonesia yaitu terdapat kemiripan aturan hukum sehingga menimbulkan masalah yaitu aturan hukum yang tumpang tindih dan tidak sinkron. Ketika aturan hukum yang baru akan dibentuk, sebaiknya tersedia tools yang dapat melakukan pemeriksaan terlebih dulu terhadap hukum lama sehingga tidak tumpang tindih dan sinkron. Knowledge graph (KG) digunakan pada penelitian ini untuk merepresentasikan hukum-hukum yang ada di Indonesia. Penelusuran KG tersebut akan dibantu dengan teknologi graph mining. Pada penelitian ini, dibahas pula aspek-aspek filsafat sains pada beberapa penelitian terdahulu yang berkaitan dengan topik penelitian ini.

SYSTEMATIC LITERATURE REVIEW OF DOCUMENTS SIMILARITY DETECTION IN THE LEGAL FIELD: TREND, IMPLEMENTATION, OPPORTUNITIES AND CHALLENGE USING THE KITCHENHAM METHOD Nazuli, Muhammad Furqan; Walhidayah, Irfan; Akhyar, Amany; Saptawati Soekidjo, Gusti Ayu Putri
Jurnal Teknik Informatika (Jutif) Vol. 5 No. 5 (2024): JUTIF Volume 5, Number 5, Oktober 2024
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2024.5.5.2444

This research conducted a Systematic Literature Review (SLR) to observe the application of graph mining techniques in detecting document law similarities. Graph mining, where nodes and edges represent entities and relations respectively, has proven effective in identifying patterns within legal documents. This review encompasses 93 relevant studies published over the past five years. Despite its potential, graph mining in the legal domain faces challenges, such as the complexity of implementation and the necessity for high-quality data. There is a need to better understand how these techniques can be optimized and applied effectively to address these challenges. This SLR utilized a comprehensive approach to identify and analyze trends, implementations, and popular domains related to graph mining in legal documents. The study reviewed trends in the number of studies, categorized the implementations, and evaluated the prevalent techniques employed. The review reveals a growing trend in the use of graph mining techniques, with a noticeable increase in the number of studies year by year. The implementation of these techniques is the most popular category, with applications predominantly in legal domains such as laws, legal documents, and case law. The most frequently used graph mining techniques involve Natural Language Processing (NLP), Information Retrieval, and Deep Learning. Although challenges persist, including complex implementation and the need for quality data, graph mining remains a promising approach for developing future information systems in law.

A systematic literature review to address overlapping laws in Indonesia Akhyar, Amany; Saptawati, Gusti Ayu Putri
Bulletin of Electrical Engineering and Informatics Vol 14, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i3.8407

The vast number of laws often result in legal uncertainty due to overlapping, conflicting, and inconsistent regulations. Identifying and resolving these overlaps is essential for ensuring legal clarity and coherence. This systematic literature review (SLR) explores technologies that have the potential to address the issue of overlapping laws in Indonesia. This study reviews numerous works on knowledge graphs (KGs) and graph mining, focusing on their potential to automate the detection of overlapping laws, thereby streamlining the process of legal harmonization. The review identifies several key research opportunities, such as refining KG construction, exploring semantic similarity measures, enhancing the interlinking of legal information, and ensuring explainability and interpretability. These opportunities promise to enhance the efficiency and effectiveness of detecting overlapping laws and contribute to a more consistent legal system in Indonesia.

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