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All Journal IJISTECH (International Journal Of Information System & Technology) G-Tech : Jurnal Teknologi Terapan IJISTECH
Alva Hendi Muhammad
Universitas Amikom Yogyakarta, Indonesia
Computer Science & IT Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Energy Engineering Social Sciences

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Prediksi Prevalensi Stunting di Indonesia dengan Ordinary Least Square (OLS) Benny Putra; Alva Hendi Muhammad
G-Tech: Jurnal Teknologi Terapan Vol 8 No 3 (2024): G-Tech, Vol. 8 No. 3 Juli 2024
Publisher : Universitas Islam Raden Rahmat, Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33379/gtech.v8i3.4623

Abstract

Stunting is a growth issue in children, a serious concern both in Indonesia and globally, affecting over 149 million children worldwide, including 6.3 million in Indonesia. Despite some reduction, achieving the national target by 2024 remains challenging. The government has issued a Presidential Regulation to address stunting, focusing on family nutrition and environmental hygiene. This study aims to predict stunting prevalence, develop a more accurate prediction model, provide a basis for policy, and contribute to the scientific literature on stunting in Indonesia. The methods used include comparing algorithms such as Neural Network (NN), RBF Network, SVR kernel RBF, and Ordinary Least Square (OLS). The evaluation shows significant performance variation: NN performs fairly well, RBF Network performs better, SVR kernel RBF also performs well, but OLS stands out with very accurate predictions, minimal error values, and high correlation.
Optimalisasi Akurasi Algoritma C4.5 dengan Metode Adaptive Boosting Memprediksi Siswa dalam Menerima Dana Pendidikan Wahyu Aji Tri Riswandhana; Alva Hendi Muhammad
G-Tech: Jurnal Teknologi Terapan Vol 8 No 4 (2024): G-Tech, Vol. 8 No. 4 Oktober 2024
Publisher : Universitas Islam Raden Rahmat, Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70609/gtech.v8i4.5612

Abstract

The importance of increasing accuracy for educational institutions in predicting the provision of educational financial assistance. To make decisions about who deserves education funding. Data processing on aid recipients can be processed into information. This study aims to improve the accuracy of the C4.5 algorithm by using adaboost to determine whether students deserve to receive educational assistance funds or not by comparing the results before and after implementing adaboost. Predicting students' eligibility for obtaining educational assistance funds using decision trees. The dataset was collected from 414 students of SMK Muhammadiyah 1 Ngoro, used for this research. The research results show an increase in accuracy of 2.69% with the application of the C4.5 algorithm which has an accuracy of 77.31%, while the accuracy with the application of Adaboost reaches 80%.
Security Infrastructure Service and Information Security Management Capability Audit to Improve System Security in Preventing Cyber Attacks using COBIT 2019 Salim Salim; Alva Hendi Muhammad
G-Tech: Jurnal Teknologi Terapan Vol 9 No 1 (2025): G-Tech, Vol. 9 No. 1 January 2025
Publisher : Universitas Islam Raden Rahmat, Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70609/gtech.v9i1.6319

Abstract

Cyber-attacks have become a serious concern in recent years, as hackers can attack systems, databases, and even steal personal data from a particular organization's database and sell it on the dark web. The individual identity data will be used to carry out various financially profitable activities such as online fraud, selling data containing detailed account information, and asking for ransom from data owners. The failure of an organization to protect data assets from cyber-attacks stems from several internal factors such as the vulnerability of the information security system it has, the less than optimal awareness of users in the organization of the importance of maintaining safe behavior in cyberspace, and routine processes related to information security that are missed. COBIT 2019 can be used as an audit framework to assess the level of maturity of the governance. Network infrastructure services in an organization can be measured in terms of the level of capability in terms of the application of technology assets and their configuration using the DSS05 and APO13 domains. This study aims to provide guidelines in assessing system security infrastructure in IT governance by assessing areas that are still not comply with the framework used, and providing recommendations for improving these inconsistencies so that the level of infrastructure capability will be maximized then.
Cluster Analysis for Performance Evaluation of Outsourcing Engineers in the Telecommunication Industry M. Rivai Hasibuan; K Kusrini; Alva Hendi Muhammad
IJISTECH (International Journal of Information System and Technology) Vol 7, No 1 (2023): The June edition
Publisher : Sekolah Tinggi Ilmu Komputer (STIKOM) Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/ijistech.v7i1.301

Abstract

The telecommunications industry relies significantly on the knowledge and contributions of engineers, who play a crucial role in designing, enhancing, and supervising diverse communication infrastructure systems. As engineers gain experience, they frequently advance to supervisory or consulting positions in which they provide expert guidance. Many businesses have delegated their recruitment processes to contract workers to optimize operational costs and boost productivity. It is essential to evaluate the performance of outsourced employees in order to determine the value they add to the organization. Performance evaluations play a vital role in evaluating factors such as attendance, skill level, burden management, and job performance. These evaluations aid in contract renewal decision-making. Various metrics emphasizing factors such as effective skills, well-defined objectives, and regular status updates can be used to determine the success of outsourced employees. This permits a comprehensive evaluation and the identification of improvement areas. In this paper, the investigation concentrates on using cluster analysis techniques, specifically partition-based algorithms such as K-means, to classify outsourced workers according to their individual skills and characteristics. Then, we compare the provided insights with the density-based algorithm DBSCAN to comprehend them. Cluster analysis is a potent method for analyzing large datasets, allowing for swift and confident conclusions. By utilizing cluster analysis, organizations can gain insight into the diverse skill sets and characteristics of their outsourced workforce, thereby improving resource allocation, task assignment, and management as a whole
Cluster Analysis for Performance Evaluation of Outsourcing Engineers in the Telecommunication Industry M. Rivai Hasibuan; K Kusrini; Alva Hendi Muhammad
IJISTECH (International Journal of Information System and Technology) Vol 7, No 1 (2023): The June edition
Publisher : Sekolah Tinggi Ilmu Komputer (STIKOM) Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/ijistech.v7i1.301

Abstract

The telecommunications industry relies significantly on the knowledge and contributions of engineers, who play a crucial role in designing, enhancing, and supervising diverse communication infrastructure systems. As engineers gain experience, they frequently advance to supervisory or consulting positions in which they provide expert guidance. Many businesses have delegated their recruitment processes to contract workers to optimize operational costs and boost productivity. It is essential to evaluate the performance of outsourced employees in order to determine the value they add to the organization. Performance evaluations play a vital role in evaluating factors such as attendance, skill level, burden management, and job performance. These evaluations aid in contract renewal decision-making. Various metrics emphasizing factors such as effective skills, well-defined objectives, and regular status updates can be used to determine the success of outsourced employees. This permits a comprehensive evaluation and the identification of improvement areas. In this paper, the investigation concentrates on using cluster analysis techniques, specifically partition-based algorithms such as K-means, to classify outsourced workers according to their individual skills and characteristics. Then, we compare the provided insights with the density-based algorithm DBSCAN to comprehend them. Cluster analysis is a potent method for analyzing large datasets, allowing for swift and confident conclusions. By utilizing cluster analysis, organizations can gain insight into the diverse skill sets and characteristics of their outsourced workforce, thereby improving resource allocation, task assignment, and management as a whole
Co-Authors Benny Putra Kusrini M. Rivai Hasibuan Salim Salim Wahyu Aji Tri Riswandhana