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All Journal BAREKENG: Jurnal Ilmu Matematika dan Terapan Simtek : Jurnal Sistem Informasi dan Teknik Komputer Inferensi Jurnal Pengabdian kepada Masyarakat Nusantara
Rahman, Gusti Arviana
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Energy Engineering Mathematics Mechanical Engineering Physics Social Sciences Transportation

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PENGGUNAAN ALGORITMA DBSCAN DALAM PENGELOMPOKAN KABUPATEN/KOTA DI SULAWESI TENGGARA BERDASARKAN INDIKATOR PENDIDIKAN Rahman, Gusti Arviana; Hasbi, Irham; Tenriawaru, Andi; Surimi, La; Alfiyan, Arif Nur
Simtek : jurnal sistem informasi dan teknik komputer Vol. 10 No. 1 (2025): April 2025
Publisher : STMIK Catur Sakti Kendari

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51876/simtek.v10i1.1546

Abstract

Pendidikan merupakan pilar utama dalam membangun kemajuan suatu bangsa serta merupakan investasi strategis bagi peningkatan kualitas sumber daya manusia. Di Indonesia, pendidikan menjadi salah satu fokus utama dalam upaya pembangunan nasional. Meskipun pemerintah telah mengimplementasikan program wajib belajar 12 tahun, ketimpangan pendidikan antara wilayah perkotaan dan pedesaan masih cukup mencolok, mencerminkan belum meratanya akses pendidikan di berbagai daerah. Penelitian ini bertujuan untuk mengelompokkan kabupaten/kota di Provinsi Sulawesi Tenggara berdasarkan indikator pendidikan menggunakan algoritma DBSCAN. Algoritma ini dipilih karena kemampuannya dalam mengenali pola, membentuk klaster secara dinamis, serta mendeteksi outlier. Hasil analisis menunjukkan pembentukan satu klaster utama dan satu outlier, yaitu Kota Kendari, yang memiliki nilai indikator pendidikan lebih tinggi dibandingkan wilayah lainnya. Temuan ini diharapkan dapat menjadi dasar pertimbangan dalam merumuskan kebijakan pendidikan yang lebih tepat sasaran.
EVALUASI MODEL PREDIKSI PRODUKTIVITAS JAGUNG DI INDONESIA MENGGUNAKAN ALGORITMA PEMBELAJARAN MESIN Hamundu, Ferdinand Murni; Rahman, Gusti Arviana; Tenriawaru, Andi; Rashid Armin
Simtek : jurnal sistem informasi dan teknik komputer Vol. 10 No. 1 (2025): April 2025
Publisher : STMIK Catur Sakti Kendari

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51876/simtek.v10i1.1559

Abstract

Ketahanan pangan merupakan isu global yang mempengaruhi banyak negara berkembang.  Jagung adalah salah satu tanaman pangan terpenting di dunia setelah padi dan gandum. Pada penelitian ini telah diterapkan teknik pembelajaran mesin untuk memprediksi data peramalam produktivitas jagung yang dapat mendukung ketahanan pangan. Algoritma yang digunakan adalah Random Forest, Boosting, dan Bagging. Penelitian ini mengevaluasi beberapa model dengan akurasi sampel. Hasilnya adalah Random forest lebih baik daripada metode yang lain berdasarkan tingkat kesalahan terendah. Hal ini ditunjukkan dengan nilai validitasnya yang paling minimum seperti MSE (6.764), MAPE (9.545), SSE (87570.9), dan R-square (0.8327575). Oleh karena itu, Random Forest dapat diandalkan untuk menyelidiki keakuratan data berkaitan dengan prediksi produktivitas jagung.
ANALYSIS OF OPTIMUM CONTROL ON THE IMPLEMENTATION OF VACCINATION AND QUARANTINE ON THE SPREAD OF COVID-19 Nuha, Agusyarif Rezka; Achmad, Novianita; Rahman, Gusti Arviana; Abdullah, Syarif; Chasanah, Sri Istiyarti Uswatun; Valentika, Nina; Nashar, La Ode
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 16 No 4 (2022): BAREKENG: Journal of Mathematics and Its Applications
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (426.143 KB) | DOI: 10.30598/barekengvol16iss4pp1139-1146

Abstract

This study constructs an SVIR-type COVID-19 spread model into a model with control variables or optimum control problems. In the formulation of the model with controls, we set four control variables, namely vaccination strategy, quarantine, reduction of vaccine shrinkage, and treatment. Pontryagin 's maximum principle is applied in the model as a sufficient condition to achieve optimum conditions for minimizing the objective function . This study uses a numerical solution to describe the theoretical results. The results showed that the control model could accelerate the decrease in the number of individuals in the infected population class. We found that vaccination is a top priority that needs to be done to reduce the number of cases of COVID-19 infection. In addition, the implementation of quarantine can also be considered to accelerate the decrease in the number of individuals infected with COVID-19.
MULTINOMIAL LOGISTIC REGRESSION MODEL USING MAXIMUM LIKELIHOOD APPROACH AND BAYES METHOD ON INDONESIA'S ECONOMIC GROWTH PRE TO POST COVID-19 PANDEMIC Purwanto, Arie; Suprayogi, Muhammad Aziz; Setiawan, Erwan; Loly, Joao Ferreira Rendes Bean; Rahman, Gusti Arviana; Kurnia, Anang
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 19 No 1 (2025): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol19iss1pp51-62

Abstract

Economic growth in Indonesia has become a major concern in the global context, especially before and after the Covid-19 pandemic. Key sectors such as tourism, manufacturing, trade and transportation have been seriously affected by restrictions on travel and economic activity imposed to control the spread of the virus. Therefore, it is considered necessary to carry out modeling to describe existing conditions. In this research, two approaches were used, namely the Maximum Likelihood approach and the Bayes approach. The use of methods in general as research material for researchers to study these two methods further. So far the algorithm used for the Bayes concept method is Markov Chain Monte Carlo with Hasting's Metropolis method. The parameter estimation results obtained from both methods are considered quite identical. However, it is necessary to pay attention to the iteration procedure that will be carried out. The selection of factors used in the iteration process is very determining in obtaining estimated parameter values. Furthermore, the results obtained so far do not contain any fundamental differences regarding economic growth in Indonesia. In general, Indonesia can be said to be stable in terms of economic growth.
Mempengaruhi Keputusan Calon Pelamar CPNS Dosen Melalui Webinar Kiat Sukses Memilih Formasi Wangsa, Arma; Naing, Ince Rezky; Yansa, Hajra; Salido, Achmad; Rahman, Gusti Arviana; Firdaus, Andi Mulawakkan; Wijaya, Ariyadi
Jurnal Pengabdian kepada Masyarakat Nusantara Vol. 5 No. 4 (2024): Jurnal Pengabdian kepada Masyarakat Nusantara (JPkMN) Edisi September - Desembe
Publisher : Lembaga Dongan Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55338/jpkmn.v5i4.4763

Abstract

There are prospective applicants for Civil Servant Candidate (CPNS) positions, particularly for lecturer roles, who still feel uncertain about the formations they plan to apply for. This uncertainty stems from several factors, including the time and selection process, the number of available positions, the number of applicants, and the passing score threshold. In light of these considerations, this article seeks to explain how the webinar on tips for successfully choosing lecturer formations influenced the decisions of prospective CPNS applicants in selecting a formation. The webinar was conducted in three main phases: pre-implementation, implementation, and evaluation. The results of this community service initiative show that the webinar influenced the decisions of prospective CPNS lecturer applicants in choosing a formation. Before attending the webinar, only about 30.8% felt confident in their decision, but after attending, this percentage increased to around 93.3%. Participants who were initially unsure about their choices became more confident after the webinar, with two main outcomes. The first was that participants became more confident in their original choice, and the second was that participants felt confident to switch to another formation.
CART and Random Forest Analysis on Graduation Status of Halu Oleo University Students Rahman, Gusti Arviana; Notodiputro, Khairil Anwar; Sartono, Bagus; Surimi, La
Inferensi Vol 8, No 3 (2025)
Publisher : Department of Statistics ITS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j27213862.v8i3.23336

Abstract

Classification and Regression Tree (CART) is a popular classification method and it is used in various fields. The method is capable to be applied on various data conditions. An alternative method of CART is random forest. These two methods of classification were studied in this paper using graduation data of Halu Oleo University. This data was interesting due to the imbalance problem existed in the data. We compared several scenarios, namely the CART and Random Forest methods, Random Forest with oversampling, and Random Forest with undersampling. There were three explanatory variables considered in the model including Study Program, GPA, and TOEFL score. The results showed that the best method to classify the student’s graduation status at Halu Oleo University is Random Forest without handling imbalanced data, as it provided the highest sensitivity. This suggests that Random Forest, even without specific adjustments for data imbalance, can effectively capture the patterns in the data and provide accurate classifications, making it a robust choice for this dataset.
Co-Authors Agusyarif Rezka Nuha Alfiyan, Arif Nur Anang Kurnia Andi Tenriawaru Ariyadi Wijaya Arma Wangsa Bagus Sartono Chasanah, Sri Istiyarti Uswatun Erwan Setiawan, Erwan Firdaus, Andi Mulawakkan Hajra Yansa Hasbi, Irham La Ode Nashar La Surimi, La Loly, Joao Ferreira Rendes Bean Naing, Ince Rezky Purwanto, Arie Rashid Armin Salido, Achmad Suprayogi, Muhammad Aziz Syarif Abdullah Valentika, Nina