Article Per Year (5 Year)
p-Index From 2021 - 2026
1.452
P-Index
This Author published in this journals
All Journal International Journal of Electrical and Computer Engineering Teknika Jupiter Jurnal Informatika Jurnal Informatika Proceeding International Conference on Information Technology and Business Jurnal Kompetitif Proceedings Konferensi Nasional Sistem dan Informatika (KNS&I) International conference on Information Technology and Business (ICITB) Jurnal Teknologi Informasi dan Bisnis Pengabdian Masyarakat Darmajaya Seminar Nasional Informatika (SEMNASIF) International Journal of Artificial Intelligence Research Prosiding Seminar Nasional Darmajaya Journal of Applied Data Sciences Jurnal Pengabdian kepada Masyarakat Jurnal Kedaymas Jurnal Publika Pengabdian Masyarakat Jurnal ABDI MERCUSUAR
Yulmaini Yulmaini
Institut Informatika Dan Bisnis Darmajaya
Agriculture, Biological Sciences & Forestry Humanities Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Medicine & Pharmacology Nursing Public Health Social Sciences Other

Published : 45 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 45 Documents
Search

 1   2   3   4   5 
Mewujudkan Kemandirian UMKM Melalui Pelatihan dan Pendampingan Pencatatan Laporan Keuangan Dan Penetapan Harga Jual Produk Yulmaini Yulmaini; Sri Lestari; Mieke Rahayu; Aswin Aswin; Fitri Agustina; Sulyono Sulyono; Ruki Rizal Nul Fikri; Ade Moussadecq; Muhammad Redintan Justin
-
Publisher : Jurnal KeDayMas: Kemitraan dan Pemberdayaan Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14414/kedaymas.v3i2.3623

Abstract

The Indonesian government has declared Covid-19 as a national disaster since April 13, 2020. Various efforts have been made by the government such as Work From Home (WFH), social distancing, and the Enforcement of Restrictions on Community Activities (PPKM) to reduce the spread of the corona virus. However, this policy has an impact on the decline in people's purchasing power. One of the sectors affected is MSMEs. This service aims to provide assistance in making financial statement bookkeeping and determining product selling prices for MSMEs in Pesawaran Regency. The three stages of activities carried out include preparation, implementation and monitoring. Preparation is carried out by conducting surveys in the field in coordination with partners. The implementation is carried out by training and mentoring using the lecture method followed by a question and answer discussion, and practicing making financial reports and calculating product selling prices. Monitoring is carried out periodically during service activities. This service results in the recording of simple financial statements, namely the cash book, accounts payable book, accounts receivable book, and inventory book, as well as calculating production costs and selling prices (hpp). Knowledge and soft skills regarding financial reports and determining product selling prices for MSME owners are needed, so that their business development can be monitored.
Alleviating cold start and sparsity problems in the micro, small, and medium enterprises marketplace using clustering and imputation techniques Lestari, Sri; Yulmaini, Yulmaini; Aswin, Aswin; Ma'ruf, Singgih Yulizar; Sulyono, Sulyono; Fikri, Ruki Rizal Nul
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 3: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i3.pp3220-3229

Abstract

Recommendation systems are often implemented in e-commerce and micro, small, and medium enterprises (MSMEs) marketplaces to improve consumer services by providing product recommendations according to their interests. However, it still faces problems, namely sparsity and cold start, thus affecting the quality of recommendations. This research proposes clustering and imputation techniques to overcome this problem. The clustering technique used is k-means, while the missing value imputation method uses average values. The imputation results are then implemented in the k-nearest neighbor (KNN) and naïve Bayes algorithms and evaluated based on performance accuracy. Experimental results show an increase in accuracy of 16.48% in the KNN algorithm from 83.52% to 100%. Meanwhile, the naïve Bayes algorithm increased accuracy by 35.30% from 64.70% to 100%.
The Implementation of AHP for Determining Dominant Criteria in Higher Education Competitiveness Development Strategy Based on Information Technology Yulmaini, Yulmaini; Sanusi, Anuar; Yusendra, M. Ariza Eka
International Journal of Artificial Intelligence Research Vol 3, No 1 (2019): June 2019
Publisher : Universitas Dharma Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (259.803 KB) | DOI: 10.29099/ijair.v3i1.85

Abstract

The existence of Higher Education has a huge role in nation and state’s life through tri dharma of Higher Education named education, research and community service. Higher Education can produce economic innovations based on knowledge so that, it will increase productivity and nation competitiveness. Higher Education must have strategies that will be carried out, therefore they are able to compete with other higher education according to stakeholder needs. The purposes of this research are to analyze 2 (two) models of information technology relations in the higher education competitiveness development strategy determining the most dominant criteria according to the higher education development direction (Relevance, Academic Atmosphere, Internal Management, Sustainability, Efficiency and Productivity, Access and Equit and Leadership). The method of this reserach is AHP method in wich the data are collected through questionnaires to respondents in collage. The criteria of this research are internal management & organization, academic atmosphere and university competitive sustainability. The results of this research are the information technology relations model with internal management, and the relation model between internal management and efficiency & productivities, and also the most dominant criteria in the higher education competitiveness development strategy are the criteria of Academic Atmosphere, Efficiency and Productivity.
Media Pembelajaran Pengenalan Perangkat Komputer Berbasis Multimedia Interaktif Yulmaini, Yulmaini; Permatasari, Yoan Desi
TEKNIKA Vol. 20 No. 2 (2026): Teknika Mei 2026 (In Progress)
Publisher : Politeknik Negeri Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5281/zenodo.18142572

Abstract

Perkembangan teknologi informasi mendorong dunia pendidikan untuk beradaptasi melalui media pembelajaran digital. Namun, pembelajaran pada jurusan Teknik Komputer dan Jaringan (TKJ) masih terkendala keterbatasan perangkat praktik dan metode pengajaran konvensional, sehingga kesempatan praktik bagi siswa terbatas dan pemahaman siswa terhadap perangkat keras komputer rendah. Penelitian ini bertujuan mengembangkan “Media Pembelajaran Perangkat Komputer dan Jaringan Berbasis Multimedia Interaktif” sebagai solusi inovatif berbasis teknologi multimedia. Aplikasi dikembangkan menggunakan Construct 2 dan diimplementasikan pada platform Android. Metode yang digunakan adalah Multimedia Development Life Cycle (MDLC) yang meliputi enam tahap: konsep, perancangan, pengumpulan bahan, pembuatan, pengujian, dan distribusi. Hasil penelitian menunjukkan aplikasi mampu menyajikan materi melalui teks, video, serta Game interaktif seperti perakitan komponen komputer dan pembuatan kabel RJ45. Pengujian menunjukkan seluruh fitur berfungsi dengan baik. Dengan demikian, aplikasi ini bertujuan sebagai media pembelajaran alternatif untuk keterbatasan perangkat praktik dan metode pengajaran konvensional dan memberikan dampak untuk meningkatkan pemahaman siswa terhadap perangkat komputer.
RankPro-M Method to Alleviate the Sparsity Problem in Collaborative Filtering Lestari, Sri; Yulmaini, Yulmaini; Irianto, Suhendro Yusuf; Sabita, Hari
Journal of Applied Data Sciences Vol 7, No 2: May 2026
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v7i2.1173

Abstract

The rapid shift from conventional commerce to online platforms has been driven by evolving consumer behavior that demands fast, accurate, and personalized services. Consequently, e-commerce has become a primary channel for product marketing and service delivery without temporal or spatial constraints. However, the continuous expansion of e-commerce platforms has led to a substantial increase in both the volume and diversity of available products, thereby complicating the task of delivering personalized recommendations aligned with user preferences. Recommender systems offer an effective solution to this challenge, with Collaborative Filtering (CF) being among the most widely adopted techniques. Despite its popularity, CF suffers from a critical limitation known as the data sparsity problem, which adversely affects recommendation accuracy and system reliability. This study proposes RankPro-M, a ranking-oriented imputation approach designed to mitigate the impact of sparsity in recommender systems. RankPro-M operates by identifying items with high rating frequency and imputing missing ratings using mode values as representations of dominant user preferences. The imputed rating matrix is subsequently processed through ranking aggregation mechanisms (Borda, Copeland, and WP-Rank) to generate item recommendations. Experimental results demonstrate that the application of RankPro-M consistently improves recommendation quality, as indicated by increased Normalized Discounted Cumulative Gain (NDCG) values across multiple evaluation scenarios. These findings confirm that RankPro-M effectively addresses data sparsity and enhances the performance of ranking-based recommender systems.
Co-Authors - - Suhada, - - -, Murhadi Ade Moussadecq Afnuhazi, Ridhyalla Andayani, Rifka Putri Andi Desfiandi Anuar Sanusi Anuar Sanusi Anuar Sanusi Apri Triansyah Ardi Dwi Saputra Aswin Aswin Aswin Aswin Aswin Aswin Aziz, RZ. Abdul Deni Dwi N.S Deni Dwi N.S Devi Kathina Rani Devi Kathina Rani, Devi Kathina Eka Yuliana Eka Yuliana Eka Yusendra, M. Ariza Elida Purba Elida Purba Fikri, Ruki Rizal Nul Fitri Agustina Fitria - Fitria - Herlina Herlina Herlina Herlina Indriyati, Lia Lia Indriyati M. Ariza Eka Yusendra M. Ariza Eka Yusendra M. Ariza Eka Yusendra, M. Ariza Eka M.Ariza Eka Yusendra Ma'ruf, Singgih Yulizar Mieke Rahayu Muhammad Redintan Justin Murhadi Murhadi Mutiara, Suci Mutmainah, Nina Netti Septina Nina Mutmainah Permatasari, Yoan Desi Pratama, Teddy Renjani, Asri Bunga Rio Kurniawan Ruki Rizal Nul Fikri Sabita, Hari Siti Kholijah Sri Karnila Sri Lestari SRI LESTARI Suci Mutiara Suci Mutiara Sulyono Sulyono, Sulyono Sylvia Sylvia Teddy Pratama Y. Suhendro Yan Aditiya Pratama Yusendra, M.Ariza Eka Zainal Ansori