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All Journal BAREKENG: Jurnal Ilmu Matematika dan Terapan Imajiner: Jurnal Matematika dan Pendidikan Matematika Euler : Jurnal Ilmiah Matematika, Sains dan Teknologi Jurnal Multidisipliner Kapalamada
Martinasari, Made Diyah Putri
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Religion Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Energy Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Mechanical Engineering Physics Social Sciences Transportation Other

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Optimasi Rute Terpendek Tempat Pelayanan Kesehatan di Tanjung Pandan dengan Algoritma Dijkstra Martinasari, Made Diyah Putri; Fatonah, Darojatun
Imajiner: Jurnal Matematika dan Pendidikan Matematika Vol 7, No 4 (2025): Imajiner: Jurnal Matematika dan Pendidikan Matematika
Publisher : Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/imajiner.v7i4.23857

Abstract

Tanjung Pandan adalah ibu kota dari Kabupaten Belitung, yang merupakan satu dari 7 (tujuh) Kabupaten/Kota yang ada di Provinsi Kepulauan Bangka Belitung. Sebagai ibu kota kabupaten, banyak mobilisasi yang dilakukan masyarakat ke Tanjung Pandan untuk berbagai kebutuhan, salah satunya adalah kebutuhan di bidang kesehatan. Penghitungan rute terpendek ke tempat-tempat pelayanan kesehatan di Tanjung Pandan diharapkan dapat memudahkan masyarakat dan wisatawan yang memerlukan pelayanan kesehatan di Tanjung Pandan atau Kabupaten Belitung. Penghitungan rute terpendek dalam penelitian ini menggunakan algoritma Dijkstra dengan melibatkan 22 (dua puluh dua) simpul yang mewakili rumah sakit, puskesmas, dan klinik di Tanjung Pandan. Pengumpulan data primer dilakukan melalui kunjungan langsung ke tempat pelayanan kesehatan dengan bantuan Google Maps dan observasi penulis. Hasil penghitungan menunjukkan tiga jenis tempat pelayanan kesehatan yang di rekomendasikan dalam penelitian ini adalah Klinik Utama dengan rute v_1 - v_5 dan jarak tempuh sejauh 450 meter, Puskesmas Tanjung dengan rute v_1 - v_6 - v_7 dan jarak tempuh sejauh 790 meter, serta Rumah Sakit Timah dengan rute v_1 - v_2 - v_4 dan jarak tempuh sejauh 800 meter.
IMPROVING FORECAST ACCURACY OF INDONESIAN AGRICULTURAL EXPORTS USING ANFIS SPLITTING RATIOS Septiarini, Tri Wijayanti; Martinasari, Made Diyah Putri
Jurnal Multidisipliner Kapalamada Vol. 4 No. 03 (2025): JURNAL MULTIDISIPLINER KAPALAMADA
Publisher : Pusat Studi Ekonomi, Publikasi Ilmiah dan Pengembangan SDM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62668/kapalamada.v4i03.1764

Abstract

Agricultural exports are highly vulnerable to global price volatility and seasonal fluctuations, creating demand for more accurate forecasting methods. This study evaluates the Adaptive Neuro-Fuzzy Inference System (ANFIS) for forecasting Indonesia’s monthly agricultural exports, addressing a gap in the literature where soft computing approaches have rarely been systematically applied. Using official trade data from 2012 to 2025, two alternative training–testing schemes (75%:25% and 80%:20%) were implemented with standard preprocessing, and forecasting accuracy was measured using RMSE, MAE, and MAPE. The results show that ANFIS delivered accuracy within widely accepted thresholds under the 75%:25% split, while accuracy declined under the 80%:20% split. Theoretically, the study contributes by clarifying conditions for reliable neuro-fuzzy forecasting and emphasizing standardized evaluation protocols. Practically, the findings provide decision-relevant insights for policymakers and exporters, supporting export target setting, forward-contract planning during volatile price swings, and logistics coordination during peak harvest seasons.
Identifying Digital Literacy Profiles in Distance Education: A K-Prototypes Clustering Approach Zili, Arman Haqqi Anna; Martinasari, Made Diyah Putri; Kharis, Selly Anastassia Amellia
Euler : Jurnal Ilmiah Matematika, Sains dan Teknologi Volume 13 Issue 3 December 2025
Publisher : Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/euler.v13i3.34568

Abstract

Education quality is one of the main focuses of Indonesia’s Sustainable Development Goals (SDGs), particularly in the goal that emphasizes equitable access and lifelong learning. Universitas Terbuka (UT) is a higher education institution that implements an open and distance learning system. This setting creates a diverse student body in terms of age, occupation, and digital literacy levels. Segmenting students based on their digital literacy is both essential and challenging, as it involves combining demographic data with daily digital behavior. This study aims to identify the digital literacy profiles of UT students using cluster analysis with the K-Prototypes algorithm. Data were obtained from a survey of 10,396 students with 42 variables. The Elbow Method analysis revealed three distinct clusters, each reflecting unique engagement profiles. The first cluster, the Engaged Evening Digital User, is active during the evening and balances work with social activities. The second cluster, the Hyper Connected Communicator, relies heavily on messaging applications for social interaction. The third cluster, the Balanced Digital Citizen, shows a more even distribution of digital use across academic, entertainment, and communication activities. These clusters predominantly comprise Generation Z individuals, many of whom are actively engaged in the private sector. The profound implications of these findings lie in their capacity to forge highly targeted strategies for digital learning, communication, and student support, thereby enhancing educational outcomes. Furthermore, this research significantly advances methodological literature by demonstrating a powerful, integrated approach to clustering mixed-type attributes, offering a more nuanced understanding of learner profiles in distance education.
MODELING AND FORECASTING MORTALITY RATES DURING THE COVID-19 PANDEMIC USING THE SECOND ADAPTED NOLFI MODEL AND AUTO ARIMA Martinasari, Made Diyah Putri; Romantica, Krishna Prafidya; Gentari, Putu Tika Dinda
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 20 No 1 (2026): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol20iss1pp0603-0618

Abstract

Modeling and forecasting mortality rates have been widely performed using various approaches. One such approach is the Second Adapted Nolfi model, which is one of three adaptations derived from the Nolfi and Generalized Nolfi models. Unfortunately, its application remains limited compared to widely used models like Lee-Carter and Cairns-Blake-Dowd. Previous studies on this model have shown satisfactory performance, particularly in residual analysis. However, those studies were conducted before the COVID-19 pandemic, and no study has yet applied it in the pandemic or post-pandemic periods. Although the pandemic may appear less relevant in 2025, the absence of such studies highlights the importance of further investigation into the model’s performance under extreme demographic conditions. This study addresses that gap by evaluating the Second Adapted Nolfi model using data from the Human Mortality Database (HMD) for the United States, the United Kingdom, and Italy. The model was applied to data up to 2019, and Auto-ARIMA was used to forecast from 2020 onward. The modeling results indicate that the logarithmic mortality curves align with established patterns, such as high rates at age 0, a decline through childhood, a sharp increase in early adulthood, and a continued rise into old age. The results also show that HMD mortality rates exceed the forecasted values for individuals aged 80 and above, suggesting increased elderly mortality during the pandemic. Three error metrics were used, yielding RMSE values from 0.01 to 0.18, MAE from 0.004 to 0.07, and MAPE from 28 to 286. Although Italy had the highest MAPE, the United States and the United Kingdom also showed notable errors. These findings reveal both the pandemic’s demographic impact and limitations of the model in capturing sudden shocks. Future studies may enhance this model through new adaptations, further comparison with other models, or alternative smoothing techniques to develop more robust mortality forecasts.
Co-Authors Arman Haqqi Anna Zili Fatonah, Darojatun Gentari, Putu Tika Dinda Romantica, Krishna Prafidya Selly Anastassia Amellia Kharis Tri Wijayanti Septiarini