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Analysis of the Impact of Network Quality, Brand Image, and Promotion on Telkomsel Package Card Purchase Decisions (Case Study on Students of SMK Brigadier General Katamso II Medan) Pranata, Bayu; Tambunan, Debora; Sibarani, Hendra Jonathan; Pasaribu, Dompak
Prosiding Seminar Nasional Ilmu Manajemen, Ekonomi, Keuangan dan Bisnis Vol. 3 No. 1 (2024): TOP 10 Best Paper, Prosiding Seminar Nasional Ilmu Manajemen, Ekonomi, Keuangan
Publisher : PT FORMOSA CENDEKIA GLOBAL

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55927/snimekb.v3i1.10826

Abstract

This study was conducted to determine the effect of network quality, brand image and promotion partially and simultaneously on purchasing decisions for Telkomsel package cards, a case study of students of SMK Brigjend Katamso II Medan. The research approach used is associative research with a quantitative approach. The population in this study consisted of all students of SMK Brigjend Katamso II Medan for the 2021/2022 academic year who used the Telkomsel provider as many as 90 students and the sampling technique used a saturated sample where the entire population was sampled, namely 90 people. This study uses primary data and secondary data. The results of this study network quality, brand image, and promotion partially have a positive effect on purchasing decisions. Simultaneous test results (F test) network quality, brand image and promotion have a positive and significant effect on purchasing decisions. The results of the determination test obtained that network quality, brand image and promotion have contributed to purchasing decisions by 66.1%, while the remaining 33.9% is influenced by other variables outside this regression model.
Investigation of Liquefaction in Balaroa, Petobo, and Jonooge (Central Sulawesi, Indonesia) Caused by the 2018 Palu Earthquake Sequence Triyono, Rahmat; Widiyantoro, Sri; Zulfakriza, Zulfakriza; Supendi, Pepen; Rahman, Aditya Setyo; Gunawan, Mohamad Taufik; Oktavia, Nur Hidayati; Rahmatullah, Fajri Syukur; Fadhilah, Fildzah Zaniati; Habibah, Nur Fani; Sativa, Oriza; Permana, Dadang; Wallansha, Robby; Octantyo, Ardian Yudhi; Persada, Yoga Dharma; Pranata, Bayu; Sujabar, Sujabar
Journal of Engineering and Technological Sciences Vol. 56 No. 3 (2024)
Publisher : Directorate for Research and Community Services, Institut Teknologi Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5614/j.eng.technol.sci.2024.56.3.1

Abstract

The liquefaction that occurred in the city of Palu on September 28, 2018, was caused by a series of significant earthquakes that took place in a relatively short time around 25 minutes after the main earthquake of magnitude 7.5. This event was followed by aftershocks of magnitudes 6.4, 6.2, and 6.1. The magnitude 6.2 aftershock occurred at 10.16 UTC, while the magnitude 6.1 aftershock occurred at 10.25 UTC. These were both located very close to the liquefaction locations in Balaroa, Petobo, and Jono Oge. We investigated the mainshock and the three aftershocks using the NCEER method based on Vs30 measurements and data from the drill liquefaction locations at Balaroa, Petobo, and Jono Oge. We found that the liquefaction was not only caused by the main earthquake but also by the subsequent aftershocks that occurred within 25 minutes after the mainshock.
Performance Evaluation of Automated and Manual Seismic Phase Picking for Rapid Earthquake Parameter Determination in the Indonesian BMKG Network Hielmy, Rayhan Irfan; Pranata, Bayu; Wijayanto; Daryono
Jurnal Meteorologi dan Geofisika Vol. 26 No. 2 (2025)
Publisher : Pusat Penelitian dan Pengembangan BMKG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31172/jmg.v26i2.1189

Abstract

Indonesia is situated at the intersection of three major tectonic plates, resulting in high seismic activity and significant earthquake vulnerability.1 Rapidly determining initial earthquake parameters—including origin time, epicenter location, depth, and magnitude—is critical for effective early warning systems. This study evaluates the reliability of automated versus fast manual picking (<3 minutes, S-wave-based) by comparing their performance against final validated results. Utilizing data from the BMKG SeisComP system for the period of May 18, 2024, to May 17, 2025, the study analyzed 2,790 seismic events across Indonesia, including low-seismicity regions such as Kalimantan. Performance was assessed across six key parameters (depth, origin time, RMS, azimuth gap, magnitude, and epicenter) using a numerical scoring system (0–100) based on deviation from validated data. The results indicate that while automated picking processed a significantly higher volume of events (1,857 events; 66.6%) compared to manual picking (327 events; 11.7%) within the target timeframe, manual picking achieved a superior 'good' quality rating (score 75–100) at 96.9%, compared to 88.5% for automated methods. Nevertheless, automated picking remains the preferred method for rapid dissemination (<3 minutes) due to its operational speed. Furthermore, the study establishes regional thresholds for the minimum seismic phases required for reliable automated picking, ranging from 8 to 16 phases depending on the region, with a national average of 15 phases.
Integrating Support Vector Regression and Kriging in Spatial Interpolation of Statistical Seismicity Parameters Sirodj, Dwi Agustin Nuriani; Aidi, Muhammad Nur; Sartono, Bagus; Syafitri, Utami Dyah; Pranata, Bayu
Indonesian Journal of Geography Vol 57, No 3 (2025): Indonesian Journal of Geography
Publisher : Faculty of Geography, Universitas Gadjah Mada

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijg.102153

Abstract

Spatial interpolation methods, such as Inverse Distance Weighting (IDW) and kriging, are commonly used in various fields. In Kriging method, semivariogram fitting is an important step, where empirical data are used to derive a theoretical model. However, when the known theoretical semivariogram model does not provide a satisfactory fit, the bias in the estimated values is increased. To address this limitation, Support Vector Regression (SVR) can be used to model the empirical semivariogram with a machine-learning method. This method has been applied in ordinary kriging interpolation for semivariogram fitting to estimate parameters related to the potential occurrence of earthquake. Specifically, the calculated parameters, based on the Gutenberg-Richter law, include the seismic activity (a-value) and rock fragility (b-value) in the Sumatera region. The results showed that SVR can model the empirical semivariogram better than the theoretical. The integration of SVR-Ordinary Kriging provides the best performance compared to other methods, such as IDW, with the smallest RMSEP values for both the b-value and a-value measuring 0.1378 and 0.7423, respectively. Aceh and Mentawai Islands tend to show low a and b values, suggesting that these areas are more vulnerable to earthquake with large magnitudes.
DNA Barcoding of Snapper Fish (Lutjanus spp.) from Kaimana and Fakfak, West Papua Ariyani, Destia Fitri; Kusuma, Wahyu Endra; Pranata, Bayu; Manangkalangi, Emmanuel; Jeni, Jeni; Toha, Abdul Hamid A.; Dailami, Muhammad
ILMU KELAUTAN: Indonesian Journal of Marine Sciences Vol 31, No 1 (2026): Ilmu Kelautan
Publisher : Marine Science Department Diponegoro University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/ik.ijms.31.1.26-36

Abstract

Kaimana and Fakfak are two regions in West Papua which have high biodiversity. The total fish production in Kaimana and Fakfak are 10,039 tons.y-1 and 17,806 tons.y-1, respectively. The snapper fish (Lutjanus spp.) is one of the important economic commodities in Kaimana and Fakfak regions. There has been a decrease in the number of exports of snapper fish in 2018 to 2019 from 4,742 tons to 4,290 tons due to overfishing and environmental pollution. This study employed DNA barcoding technology to identify the species of snapper fish collected from Kaimana and Fakfak. The DNA isolation was conducted by using genomic DNA mini kit (tissue) and the amplification of COI gene with Go Taq green master mix. Agarose gel electrophoresis was used to visualize the PCR product.  A total of 16 sequences with length 654 base pairs of COI gene were identified as five species of Lutjanidae, which were Lutjanus decussatus, L. gibbus, L. quinquelineatus, L. malabaricus, and L. johnii. Homology analysis with BLAST NCBI and BOLD System showed that all samples have similarity of 99.08-100% and query cover of 93-100%. Relationship analysis using phylogenetic tree and genetic distances showed results of intraspecific close relatives (0.001-0.016) and interspecific distant relatives (>0.1000). The phylogenetic tree illustrated that all species of Lutjanidae are separated into monophyletic clades. DNA barcoding technology successfully identified the snapper fish collected from Kaimana and Fakfak.