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APPLICATION OF FUZZY ANALYTICAL NETWORK PROCESS IN DETERMINING THE CHOICE OF AREAS OF INTEREST Tiara, Dinda; Sulistianingsih, Evy; Perdana, Hendra; Satyahadewi, Neva; Tamtama, Ray
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 17 No 4 (2023): BAREKENG: Journal of Mathematics and Its Applications
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol17iss4pp2253-2262

Abstract

The Untan Statistics Study Program offers students a choice of areas of interest to develop competencies, attitudes, and skills. This study aims to analyze the decision to determine the choice of field of interest according to lecturers and students using the Fuzzy Analytical Network Process (FANP) method. A combination of ANP methods and Fuzzy logic, FANP is used to model and analyze complex networks of several factors determining the choice of areas of interest. The step in this study begins with the determination of the criteria and sub-criteria used for tissue formation. Then a comparison was carried out in pairs using the Fuzzy scale, so that the calculation of the global weight value of each criterion and sub-criteria was obtained. The resulting weight can be used for decision making. Data in research affects the opinions of lecturers and students. The decision obtained using the FANP method in this study is in the opinion of lecturers and students that the fields of business and finance are priority alternatives with the highest weight of 44.5%. The second priority with a weight of 37.5%, namely social and industrial interests, and the environmental and disaster sector occupies the last priority with a weight of 18%.
Pelatihan Infografis Untuk Pegawai PPN Pemangkat Martha, Shantika; Debataraja, Naomi Nessyana; Rizki, Setyo Wira; Imro'ah, Nurfitri; Perdana, Hendra; Kusnandar, Dadan; Satyahadewi, Neva; Tamtama, Ray
Insan Cita : Jurnal Pengabdian Kepada Masyarakat Vol. 7 No. 1 (2025): Februari 2025-Insan Cita: Jurnal Pengabdian Kepada Masyarakat
Publisher : Universitas Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32662/insancita.v7i1.2658

Abstract

PPN Pemangkat sebagai sentra perikanan mempunyai beberapa keunggulan, yaitu lokasi strategis, dekat dengan fishing ground dan daerah pemasaran. Dengan berbagai keunggulan tersebut diharapkan dapat meningkatkan kualitas perekonomian masyarakat sekitar. Pentingnya ketersediaan informasi tentang PPN Pemangkat untuk masyarakat dapat menjadi faktor pendukung untuk meningkatkan kualitas perekonomian masyarakat yang terhubung dengan keberadaan PPN Pemangkat seperti nelayan. Infografis sangat diperlukan untuk penyajian data di PPN Pemangkat. Baik itu data tentang kapal, nelayan maupun hasil tangkapan. Infografis dapat menyederhanakan informasi yang rumit, sehingga informasi data lebih dapat dipahami untuk semua kalangan. Untuk itu pelatihan infografis bagi pegawai PPN Pemangkat sangat diperlukan. Hasil dari kegiatan ini yaitu bertambahnya pengetahuan serta kemampuan pegawai PPN Pemangkat dalam mengolah data melalui pembuatan infografis menggunakan excel.
EFEKTIVITAS PELATIHAN POWER BI DALAM MENINGKATKAN LITERASI DATA ADMIN SATU DATA KALIMANTAN BARAT Neva Satyahadewi; Evy Sulistianingsih; Shantika Martha; Nurfitri Imro'ah; Hendra Perdana; Wirda Andani; Ray Tamtama; Yuyun Eka Pratiwi; Muhammad Fikri; Pitriani; Annisa Auliarahmi; Nazwa Nursyifa; Yohanna Gabriel Richsita; Louis Putra Jaya; Jessica Audrey Valeria
Dianmas Bhakti: Jurnal Pengabdian pada Masyarakat Vol 3 No 1 (2026): Dianmas Bhakti: Jurnal Pengabdian pada Masyarakat
Publisher : LPPM Universitas Panca Bhakti

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54035/dianmas.v3i1.626

Abstract

This Community Service Program (PKM) aimed to enhance data literacy and information visualization skills among Satu Data administrators of local government agencies (OPD) through Microsoft Power BI training at the West Kalimantan Provincial Communication and Information Agency (Diskominfo). The program was implemented through preparation, face-to-face training, and evaluation stages using pre-test and post-test instruments. The training covered fundamental concepts of data analysis, data visualization techniques, and hands-on dashboard development using regional sectoral data. The results of the paired sample t-test analysis indicated a statistically significant improvement between participants’ pre-test and post-test scores, demonstrating the effectiveness of the training. Furthermore, analysis using Partial Least Squares Structural Equation Modeling (PLS-SEM) revealed that training material quality had a positive and significant effect on participants’ learning outcomes, while other supporting factors such as training duration, facilitator performance, and technical aspects did not show significant effects. These findings highlight that well-structured and relevant training materials play a critical role in improving participants’ competencies. Overall, the program contributed to strengthening analytical skills and supporting the implementation of the Satu Data Indonesia policy toward transparent and evidence-based data governance
Classification of Pension Benefit Adequacy for Civil Servants in Pontianak City Using the C4.5 Algorithm Indah Maharani, Dwi; Tamtama, Ray
STATMAT : JURNAL STATISTIKA DAN MATEMATIKA Vol 8 No 1 (2026)
Publisher : Department of Mathematics, Faculty of Mathematics and Natural Sciences, Universitas Pamulang, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32493/sm.v8i1.54960

Abstract

The development of information technology has encouraged the use of data-driven analysis to support decision-making in the governmental sector, including within the civil service pension system. This study aims to classify the adequacy of pension benefits received by retired Civil Servants in Pontianak City using the C4.5 algorithm. Pension benefit adequacy is determined by comparing the amount of basic pension received with household expenditures, which are calculated using the average monthly per capita expenditure in Pontianak City. The dataset consists of 322 retirees with variables including retirement age, years of service, last education, rank, and number of dependents. The model was developed using several training–testing data proportions, namely 70:30, 75:25, and 80:20. Model evaluation was conducted using accuracy, sensitivity, and specificity based on the confusion matrix. The results show that the 80:20 proportion produces the best model, achieving 100% accuracy, 100% sensitivity, and 100% specificity. The generated decision tree indicates that the most influential variable is the number of dependents, followed by rank and years of service. These findings suggest that household expenditure burdens and employment characteristics play a crucial role in determining pension adequacy. The resulting model is expected to assist pension management institutions in formulating data-driven policies to improve the welfare of retirees.