Claim Missing Document
Check
Articles

Efforts to Increase Electricity Sales at Pln Ulp Belawan Customer Service Zone Using AHP-ANP Method Implementation Robby Sepriadi; Mohammad Isa Irawan
Journal of Research in Social Science and Humanities Vol 5, No 1 (2025)
Publisher : Utan Kayu Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47679/jrssh.v5i1.281

Abstract

Abstract Electricity sales represent a crucial aspect of the business operations of the Perusahaan Listrik Negara (PLN). PLN is required to continuously improve its electricity sales strategies and enhance its services to meet the growing demands of its customers. PLN’s Customer Service Unit (ULP) Belawan is located in the northern part of Medan City, North Sumatra Province. PLN ULP Belawan serves a total of 75,931 customers with a total installed capacity of 220.28 MVA. In 2023, electricity sales reached 484.86 GWh, which is 105.63% of the target. The sales target for 2024 has been set at 524.46 GWh. This thesis aims to present the results of customer zoning prioritization that can be used to improve service delivery more effectively within PLN ULP Belawan. Through the weighting process using Analytic Hierarchy Process (AHP) and Analytic Network Process (ANP), as multi-criteria decision-making methods, were to optimize criteria and contribute to enhancing electricity sales performance. Each customer zone was evaluated based on several criteria, including system reliability, electricity usage regulation (P2TL), improvement of disturbance and complaint services, replacement of electricity meters, enforcement of the 720-hour usage regulation, connection criteria, cash-in performance, and accurate meter reading. The research successfully identified customer zones based on priority criteria. For the criterion of Electricity Usage Regulation (P2TL), the total energy recorded was 341,841 kWh across 173 kWh meters. Of this total, Zone 12 contributed the most with 277,019 kWh, followed by Zone 14 with 43,098 kWh, and Zone 6 with 20,914 kWh. In the Accurate Meter Reading criterion, the total energy achieved was 157,311 kWh from 815 kWh meters. Zone 14 contributed the highest amount at 67,564 kWh, followed by Zone 12 with 48,580 kWh, and Zone 6 with 41,167 kWh. Meanwhile, for the criterion of Electricity Meter Replacement, the total energy gained was 65,559 kWh from 505 kWh meters, with the largest contribution coming from Zone 6 (26,910 kWh), followed by Zone 12 (26,688 kWh), and Zone 14 (11,961 kWh). 
The Rate of Seller Correctness in Naming Batik Solo Pattern: Studied in Indonesia Online Marketplace Berlian Rahmy Lidiawaty; Mohammad Isa Irawan; Raden Venantius Hari Ginardi
Jurnal Sosial Humaniora 2020: Special Edition 2020
Publisher : Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j24433527.v0i1.6780

Abstract

Every pattern in batik Solo has a different meaning that affects its use. Thereare patterns for cultural ceremonies, including funerals. Therefore, the sellerof batik Solo’s product has to be able to give the right title as the name of itsproduct in the online marketplace, so the customer will not miss wear thebatik in the wrong ceremony. According to the importance of naming batikSolo’s product, this study aims to assess the accuracy of batik Solo patternnaming by sellers in Indonesian online marketplace. Thus the result of it canbe used by the buyer as a recommendation where is the best marketplace topurchase the batik Solo product. First, the study collects the images samplefrom four biggest marketplaces in Indonesia; Tokopedia, Bukalapak, Shopeeand Lazada. The sample was collected by inputting the name of batik Solopattern in the marketplace’s search bar. The keywords that have been usedto collect the sample are batik Parang, batik Truntum, batik Sawat, batikKawung and batik Slobog. Those are the kinds of batik Solo pattern that hasdifferent expedience from each other. After 834 samples have been collected,the study assigns whether the seller give the correct name to the batik Soloproduct or not. The result of this study is the correctness percentage (CP) ofthe seller in naming their batik Solo product. In general, the CP is 82,13%,Marketplace with the highest CP is Lazada (95,42%) and the highest CP ofpattern is Parang (91%). Besides that, the study also rates marketplace withthe parameter of Best Marketplace by Pattern (BMP) and Best Marketplaceof Category (BMC). The study divided the parameters to rate marketplacebecause the best marketplace that has the highest correctness percentage inone category of a pattern is not always a marketplace that has the highestcorrectness percentage in that pattern.
Embedding Struktur Sekunder lncRNA Berbasis Variational Autoencoder untuk Klasifikasi Kanker Hati dengan GraphSAGE Theodora Tantri Trisnawati; Mohammad Isa Irawan
Euler : Jurnal Ilmiah Matematika, Sains dan Teknologi Volume 14 Issue 2 August 2026
Publisher : Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/euler.v14i2.40387

Abstract

Identifying liver cancer-associated lncRNAs is a classification problem that can leverage various biological characteristics, such as secondary structure, expression levels, and molecular associations with RNA-binding proteins (RBPs). This study develops a VAE-GraphSAGE framework to integrate this information within a heterogeneous graph. A Variational Autoencoder (VAE) is employed for representation learning to extract lncRNA secondary structure embeddings through a reconstruction process that does not rely on class labels. These embeddings are combined with expression features to form node features, while data on lncRNA similarity, lncRNA-RBP interactions, and protein-protein interactions (PPI) are used to construct the heterogeneous graph. Subsequently, GraphSAGE serves as the classifier, utilizing inter-node relationship information to classify the lncRNAs. The dataset comprises 1,000 lncRNAs (400 positive and 600 negative) and 85 RBPs (as protein nodes), with a training-validation-testing split of 70:15:15. Comparative results indicate that VAE-GraphSAGE achieves the highest recall among the tested models (outperforming AE-GraphSAGE, MLP-GraphSAGE, CNN-GraphSAGE, and ResNet-GraphSAGE); meanwhile, MLP-GraphSAGE yields the highest accuracy, precision, specificity, and F1-score, and CNN-GraphSAGE produces the highest ROC-AUC. Furthermore, using the same VAE embeddings, VAE-GraphSAGE demonstrates superior performance compared to VAE-HGT and VAE-RGCN across the evaluated classification metrics. These results demonstrate that the VAE effectively generates secondary structure representations that facilitate the detection of positive lncRNAs, while GraphSAGE successfully leverages biological relationships within the heterogeneous graph for the classification task.
Co-Authors AA. Masroeri Abduh Riski, Abduh Adrianus Bagas Tantyo Dananjaya Akhmad Arif Junaidi Alan Catur Nugraha Alexander Setiawan Alvida Mustika Rukmi Amira, Siti Azza Andreas Handojo Anindita Sharkar Antonio Galileo Tando Ari Kusumastuti Arie Dipareza Syafei Arifah, Enny Durratul Auliya Rahmayani Baiq Findiarin Billyan Berlian Rahmy Lidiawaty Chyntia Kumalasari Puteri Danang Wahyu Wicaksono Daniel Happy Putra Darmaji, Darmaji Darmawan, Didiet Edi Satriyanto Ekky Hidma Octia Rahmah Elly Matul Imah Elnora Oktaviyani Gultom Elsen Ronando Erna Apriliani Fahim, Kistosil Fendhy Ongko Giandi, Oxsy Ginardi, Raden Venantius Hari Hadi Prasetiya Haloho, Freddi Hartanto Setiawan Hendy Hozairi Imam Mukhlash Imam Mukhlash Ira Puspitasari Ketut Buda Artana Khilmy, Akhmad Ku Khalif, Ku Muhammad Naim Mahardika, Kadek Eri Mahdiyah, Umi Mardlijah - Maulana, Muhammad Agung Adi Mey Lista Tauryawati Mohamad Muhtaromi Mohammad Hamim Zajuli Al Faroby Mohammad Iqbal Mohammad Jamhuri Mohd Aziz, Mohd Khairul Bazli Muchamad Jati Nugroho Muhammad Ahnaf Amrullah Muhammad Athoillah, Muhammad Muhammad Fakhrur Rozi Muhammad Hajarul Aswad Muhammad, Noryanti Muhammad, Noryanti binti Mujiono, Edo Priyo Utomo Putro Ni Nyoman Tri Puspaningsih Notopramono, Hanna Nugraha, Arma Perwira Nurul Anggraeni Hidayati NURUL HIDAYAT Nurul Hidayat Pratama, Qoria Yudi Putri, Endah R.M. Putri, Endah Rokhmati Merdika Putris , Nadhifa Afrinia Dwi Rasyadan Taufiq Probojati Resi Arumin Sani Rita Ambarwati Rita Ambarwati Sukmono Robby Sepriadi Robin Wijaya, Robin Ronando, Elsen Rukmini, Meme Santoso Santoso Santoso Santoso Setiawan, Muhammad Nanda Setumin, Samsul Shahab, Muhammad Luthfi Siti Maghfiroh Soetrisno Soetrisno Sulastri Sulastri Theodora Tantri Trisnawati Titin J. Ambarwati Victory Tyas Pambudi Swindiarto YAN ADITYA PRADANA Yongky Ujianto Yuda Dian Harja Zulfa Afiq Fikriya