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Analisis Klasterisasi Patok Jalan Berbasis Geospasial Menggunakan K-Means dan Evaluasi Davies-Bouldin Dien, Habibie Ed; Ratsanjani, M. Hasyim; Saputra, Agung Adi; Noprianto; Ririd, Ariadi Retno Tri Hayati; Nugraha, Bagas Satya Dian
Jurnal Pekommas Vol 9 No 2 (2024): Desember 2024
Publisher : Sekolah Tinggi Multi Media “MMTC” Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56873/jpkm.v9i2.5471

Abstract

Geographic Information System (GIS) has become a very important tool for spatial analysis and decision making in various fields. In this paper, the analysis process of grouping Kilometer Hectometer (KM/HM) road milestones use the K-Means method in the context of GIS. The KM/HM road milestones are road equipment made of concrete or signboards equipped with texts containing information about the distance and the name of the city to be traveled by road users. The length of the road that has such a long distance will make it difficult to maintain and to manage the road milestones that are scattered throughout the road. Currently, the process of mapping the location of the road milestones is still being carried out using the conventional method, in which the survey officer records the data on paper and measures it with the vehicle's odometer. However, this method often causes data loss, errors in determining the location, and lacks photographic evidence as a reference for assessing the condition of the road milestones. The role of GIS for survey officers is to map the road milestones and to visualize the road milestone data. The main objective is to gain meaningful insights from the spatial distribution of these road milestones, which will assist in better navigation and infrastructure planning. The K-Means method separates clusters from KM/HM road milestones which are identified based on geographical proximity. To assess the quality of these clusters, the evaluation is conducted using the Davies-Bouldin index (DBI) which provides a quantitative measure of inter-group similarity and within-group dissimilarity. For officers, it can be useful to find location points for the road milestones that have high damage conditions to prioritize to be repaired first. Based on the test results using DBI, it produces a value close to zero, which is equal to 0.1656, indicating that the clusters formed have very good quality.
State of Charge Estimation on Lithium-Ion Batteries Using Particle Swarm Optimization Method Dewanto, Muhammad Ridho; Saputra, Riza Hadi; Sugiarto, Kharis; Saputra, Agung Adi
ELKHA : Jurnal Teknik Elektro Vol. 17 No.1 April 2025
Publisher : Faculty of Engineering, Universitas Tanjungpura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26418/elkha.v17i1.90020

Abstract

Lithium-ion battery management is crucial as their use grows in devices and electric vehicles. A key aspect is State of Charge (SoC) estimation, which indicates the battery's charge level at any given time. This research aims to develop a method that can provide accurate SoC estimates for Li-ion batteries using the Particle Swarm Optimization (PSO) method. In this research, a 12V 8.4 Ah Lithium-Ion battery was used as a test subject, utilizing a voltage sensor, ACS712 sensor, and LM35 temperature sensor to measure key parameters such as voltage, current, and temperature. The PSO approach was chosen because of its ability to find optimal solutions in complex search spaces, such as SoC estimation in batteries. Through a combination of the PSO algorithm and data generated from sensors, it is hoped that the SoC estimates produced can improve battery usage efficiency, extend service life, and increase the performance of systems that depend on batteries. PSO can provide more accurate predictions with smaller errors, both in terms of the RMSE value of 0.0391 and the MAPE value of 12.028%. The high accuracy of 87.972% of PSO also shows that this method is reliable for applications that require precise SoC predictions. It is hoped that the results of this research can become a basis for further research in the field of battery management and metaheuristic algorithm optimization. After all, this research aims to enhance battery management systems and deepen understanding of PSO-based SoC estimation.
Efektivitas Restorative Justice Dalam Mengurangi Tindak Pidana Di Tinjauan Dari Perspektif Kejaksaan Muhdor, Algifari Malhanie; Saputra, Agung Adi
Innovative: Journal Of Social Science Research Vol. 4 No. 6 (2024): Innovative: Journal Of Social Science Research
Publisher : Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/innovative.v4i6.16250

Abstract

Penelitian ini bertujuan untuk mengevaluasi efektivitas restorative justice dalam mengurangi tindak pidana dengan fokus pada perspektif kejaksaan di Indonesia. Restorative justice merupakan pendekatan alternatif yang menekankan pemulihan hubungan antara pelaku dan korban serta mendorong reparasi atas kerugian yang ditimbulkan, dibandingkan dengan sistem peradilan tradisional yang lebih berorientasi pada hukuman. Metodelogi penelitian yang digunakan adalah kajian pustaka (library research) yang merupakan metode pengumpulan dengan menganalisis beberapa kajian teori yang berhubungan dengan topik yang diteliti. Hasil penelitian menunjukkan bahwa penerapan restorative justice di tingkat kejaksaan tidak hanya meningkatkan kepuasan korban, tetapi juga berpotensi menurunkan tingkat pengulangan kejahatan (recidivism). Meskipun demikian, terdapat tantangan dalam implementasi, termasuk keraguan dari pihak jaksa dan kurangnya dukungan regulasi. Penelitian ini menyimpulkan bahwa untuk memaksimalkan efektivitas restorative justice, diperlukan kebijakan yang mendukung serta pelatihan bagi jaksa dalam memahami dan menerapkan prinsip-prinsip restorative justice. Diharapkan temuan ini dapat memberikan kontribusi yang signifikan bagi perkembangan sistem peradilan tindak pidana di Indonesia agar lebih adil dan responsif