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All Journal JURNAL PENGABDIAN KEPADA MASYARAKAT Jurnal Mantik Penusa The IJICS (International Journal of Informatics and Computer Science) Jurnal Mantik TRIDARMA: Pengabdian Kepada Masyarakat (PkM) INFOKUM BERNAS: Jurnal Pengabdian Kepada Masyarakat Jurnal Pengabdian kepada Masyarakat Nusantara Jurnal Media Informatika Jurnal Info Sains : Informatika dan Sains Jurnal Sistem Informasi Triguna Dharma (JURSI TGD) Jurnal ICT : Information and Communication Technologies
Wira Apriani
STMIK Pelita Nusantara
Religion Agriculture, Biological Sciences & Forestry Arts Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Languange, Linguistic, Communication & Media Library & Information Science Mathematics Mechanical Engineering Neuroscience Nursing Public Health Social Sciences Veterinary Other

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CLUSTERIZATION OF DIABETES HEALTH INDICATORS WITH K-MEANS CLUSTER ALGORITHM Yuda Perwira; Wira Apriani; Ahmad Zein; Sinta Lia Alfaris
INFOKUM Vol. 10 No. 03 (2022): August, Data Mining, Image Processing, and artificial intelligence
Publisher : Sean Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (481.224 KB)

Abstract

Diabetes is one of the most common chronic diseases in the world, affecting millions of people every year and placing a significant financial burden on the economy. Diabetes is a serious chronic disease in which individuals lose the ability to effectively regulate blood glucose levels, and it can lead to decreased quality of life and life expectancy.The purpose of this study is to cluster diabetes health to be able to cluster quickly whether a person has diabetes, or prediabetes or is free from diabetes so that diabetes can be anticipated as early as possible. The data used in this study is the result of a survey from the US Behavioral Risk Factor Surveillance System (BRFSS) in 2015 which contains a net data collection of 253,680 survey responses to the CDC's 2015 BRFSS. The target variable Diabetes_012 has 3 classes. 0 for no diabetes or only during pregnancy, 1 for prediabetes, and 2 for diabetes.The method used in this study is the K-Means Clustering method where this method has been quite successful and is widely used by many researchers to cluster and predict, indicators of a person's diabetes health can be grouped into 3 groups, namely the health of people without diabetes, the health of people with prediabetes. and the health of people with type 2 diabetes, as for the results of the clustering of 2349 data, there are 235 people with health without diabetes, 1816 people with prediabetes health conditions and 298 people with type 2 diabetes.
PREDICTION OF STUDENTS WORKING ACCORDING TO COMPETENCY WITH THE C4.5 ALGORITMA ALGORITHM wira apriani; Yuda Perwira
INFOKUM Vol. 10 No. 4 (2022): October, computer, information and engineering
Publisher : Sean Institute

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Abstract

In this digital era, data is very important to be extracted in order to mine knowledge in it, the resulting data is then used to make preparations, or decisions for the future in order to get the best planning, in universities there are a lot of data that can be mined, one of which is alumni tracer data, alumni tracer data can be used to determine the characteristics of students who work according to their competencies or not by using data mining techniques, the purpose of this study is to determine the characteristics of students who after graduation can work according to their competencies , if these characteristics or criteria can be known well, then the student department can direct students who are actively learning to be able to pursue these criteria so that later these students can work according to their competencies and according to their interests. commitment to produce graduates as superior human resources who work according to their competencies. The stages of this method are collecting data from the alumni tracer questionnaire from STMIK Pelita Nusantara then processing it with data mining techniques, namely data selection, data cleaning, data transformation, then the data is processed by data mining technique classification decision tree algorithm C4.5 then evaluation and simulation with rapid miner to validate manual calculations then build the system and implement the system.
Pemasaran Tas Purun Berbasis Web Yuda Perwira; Wira Apriani; Fiqri Irhami; Kicky Maulana
Jurnal Pengabdian kepada Masyarakat Nusantara Vol. 3 No. 2.2 (2023): Jurnal Pengabdian kepada Masyarakat Nusantara
Publisher : Cv. Utility Project Solution

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Abstract

In this digital era, business people must be creative and innovative in marketing their products to be able to survive against competitors and get good profits, one of which is to use information technology to transact or introduce their products. Nasti Purun is a housing business in the Perbaungan area that produces purun into bags. , hats and other products made from purun, the purpose of this service is to create a website to be able to transact online between nasti purun and consumers and create a digital catalog so that it can be easily accessed by customers without having to come to a location and can reach a wider market again, so that businesses can develop and create jobs for local residents.
Decision Support System for Determining the Best Homeroom Teacher at SMA Negeri 1 Perbaungan Using the Simple Additive Weighting Method Wira Apriani
Jurnal ICT : Information and Communication Technologies Vol. 13 No. 1 (2022): April, Jurnal ICT : Information and Communication Technologies
Publisher : Marqcha Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (364.724 KB) | DOI: 10.35335/jict.v11i2.15

Abstract

Decision Support System (DSS) is a procedure-based model to process data and assist management in making decisions. Computer-based Decision Support System to improve decision-making ability to solve quasi-structured or unstructured problems. One of the methods that can be used to solve problems in this DSS is the Simple Additive Weighting (SAW) method. The SAW method is a method that uses weighted addition. With this method the optimal solution is sought from a number of alternatives and certain criteria. This application is desktop-based so that it can be used by users and aims to make it easier for users to need information and solutions to help choose the best homeroom teacher
Application Of Data Mining For Prediction Of Students Out Of College With The Method Algorithm C4.5 suandi daulay; wira apriani; yuda perwira
Jurnal ICT : Information and Communication Technologies Vol. 13 No. 1 (2022): April, Jurnal ICT : Information and Communication Technologies
Publisher : Marqcha Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (454.491 KB) | DOI: 10.35335/jict.v13i1.28

Abstract

This research was conducted to predict students dropping out of private universities, the student department needs to pay attention to students who have the potential to drop out so that they can be detected faster to make an approach with students so they don't drop out of college, with the help of data mining so that data -The data collected is useful information and with the C4.5 method so that predictions become accurate to detect students who have the potential to drop out of college. As for the results of this study, it is known that the most influential variable for students dropping out of college is marked by UKT Not Current Then Often Absent Then Gender Male whose graduation year is not recently graduated (not fresh graduate)
Analysis Of Student Sentiment On Lecturers Teaching Using The Fuzzy Tsukamoto Method Wira Apriani; Sulastri Sulastri; Finna Maulidina
Jurnal Info Sains : Informatika dan Sains Vol. 13 No. 02 (2023): Jurnal Info Sains : Informatika dan Sains , Edition September  2023
Publisher : SEAN Institute

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Abstract

Analysis of student sentiment on lecturer teaching performance This study aims to analyze student sentiment on lecturer teaching performance in a good university. The method used in this research is sentiment analysis using the Fuzzy Tsukamoto Method. The data were obtained from the results of a questionnaire conducted on students regarding the teaching performance of lecturers in the last semester. The data is then processed and analyzed using a classification algorithm to classify student sentiment into positive, negative, or neutral towards lecturer teaching performance. is student sentiment towards lecturers, from the teaching quality testing data = 2 positive words (8), Material Availability = 2 neutral words (7), Student and lecturer interaction = 1 negative word (4), Lecturer Feedback Quality = 2 negative word (3) from the input the output result is a value of 3 and is in the negative set, so the result of the test is negative sentiment
Penerapan Metode K-Means Clustering Dalam Analisis Profil Konsumen Untuk Strategi Manajemen Pemasaran Wira Apriani; Yuda Perwira; Refin Herissandi; Septian Jordan Purba
Jurnal Sistem Informasi Triguna Dharma (JURSI TGD) Vol. 5 No. 1 (2026): EDISI JANUARI 2026
Publisher : STMIK Triguna Dharma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53513/jursi.v5i1.12492

Abstract

Segmentasi konsumen merupakan pendekatan penting dalam manajemen pemasaran untuk memahami karakteristik pelanggan dan merumuskan strategi yang tepat sasaran. Namun, tidak semua organisasi memiliki data transaksi yang lengkap, sehingga pemanfaatan data profil pelanggan non-transaksional menjadi alternatif yang relevan. Penelitian ini bertujuan untuk melakukan segmentasi konsumen berdasarkan profil pelanggan menggunakan metode K-Means Clustering sebagai dasar penyusunan strategi manajemen pemasaran. Penelitian ini menggunakan pendekatan kuantitatif dengan teknik data mining. Dataset yang digunakan adalah dataset publik Customers Purchase Behavior dari Kaggle yang terdiri dari 72.637 data pelanggan. Variabel yang dianalisis meliputi lifestage dan premium customer. Data kategorik ditransformasikan melalui proses encoding dan dinormalisasi sebelum diterapkan algoritma K-Means Clustering. Hasil penelitian menunjukkan bahwa konsumen dapat dikelompokkan ke dalam tiga segmen utama dengan karakteristik yang berbeda berdasarkan tahapan kehidupan dan preferensi harga. Segmentasi ini mencerminkan perbedaan sensitivitas harga dan potensi nilai pelanggan pada masing-masing kelompok. Penelitian ini menyimpulkan bahwa metode K-Means Clustering efektif digunakan untuk segmentasi konsumen berbasis data profil pelanggan non-transaksional. Hasil segmentasi dapat dimanfaatkan sebagai dasar pengambilan keputusan dalam penyusunan strategi manajemen pemasaran yang lebih terarah dan berbasis data.
PENGUATAN TATA KELOLA KEUANGAN LINGKUNGAN MELALUI SISTEM LAPORAN KEUANGAN DIGITAL PADA KOMPLEK GRAND MUTIARA RESIDENCE Wira Apriani
JURNAL PENGABDIAN KEPADA MASYARAKAT Vol. 32 No. 1 (2026): JANUARI-MARET
Publisher : Universitas Negeri Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24114/jpkm.v32i1.72437

Abstract

Environmental financial management is an important aspect in supporting the sustainability of operations and shared services within residential communities. However, financial records that are still managed manually often lead to problems such as disorganized data, limited transparency, and low accountability in the management of community funds. This community service activity aims to strengthen environmental financial governance through the implementation of a digital financial reporting system for the management of Grand Mutiara Residence. The implementation method includes partner needs analysis, design of a digital financial reporting system, system implementation, training and assistance for administrators, and evaluation of program implementation. The digital financial reporting system is used to record community cash inflows and outflows and to generate periodic financial reports. The results show that the implementation of a digital financial reporting system improves the orderliness of financial records, facilitates report preparation, and enhances transparency and accountability in the management of community funds. In addition, administrators demonstrated improved capability in independently managing finances using the digital system. Therefore, this community service activity makes a tangible contribution to strengthening more orderly and sustainable environmental financial governance.
Random Forest Model for Food Security Index Prediction Using Climate and Socioeconomic Panel Data in North Sumatra Wira Apriani; Rizki Fadila Nasution; Rangga Pohan; Tyo Rizki Ramadhan
The IJICS (International Journal of Informatics and Computer Science) Vol. 10 No. 2 (2026): July
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/ijics.v10i2.9998

Abstract

This study develops and evaluates a Random Forest Regression model for predicting the Food Security Index (IKP) using climate and socioeconomic panel data from 33 regencies/cities in North Sumatra, with Deli Serdang Regency as the principal case discussed. The predictors comprise poverty rate, gross regional domestic product (GRDP) per capita, annual rainfall, and annual rainy days. The common modeling period was 2020–2021. Of 66 potential regency-year observations, eight lacked IKP values in 2021, leaving 58 complete observations. To account for the panel structure, model performance was evaluated using nested group-aware cross-validation, with regency/city as the grouping variable. Across five outer folds, Random Forest achieved RMSE 8.450 ± 2.703, MAE 6.289 ± 2.018, and R² −0.501 ± 1.500. Linear regression obtained RMSE 8.282 ± 2.431, MAE 6.401 ± 2.262, and R² −0.363 ± 1.192, while a single decision tree obtained RMSE 9.821 ± 3.482, MAE 8.030 ± 2.849, and R² −0.972 ± 1.871. Thus, no model demonstrated strong or stable out-of-group generalization. Repeated group-aware permutation analysis identified poverty rate as the most consistent predictor. The results are predictive and associational, not causal, and the present model should be regarded as an exploratory proof of concept requiring larger, spatially resolved data for robust regional forecasting.
Penerapan Metode Clustering dalam Analisis Aktiva Tetap Berbasis Objek untuk Efektivitas Audit Internal Petti Indrayati Sijabat; Eviyanti Br Barus; Wira Apriani; Flora Br Lumban Toruan; Chindy L Ritonga
Jurnal Media Informatika Vol. 6 No. 3 (2025): Jurnal Media Informatika
Publisher : Lembaga Dongan Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55338/jumin.v6i3.6523

Abstract

Tujuan penelitian ini adalah untuk meningkatkan efektivitas audit internal dengan menggunakan metode pengelompokan untuk menganalisis aset tetap berbasis objek. Pengelompokan aset yang kurang optimal berdasarkan atribut terkait, termasuk umur ekonomis, nilai buku, lokasi, dan jenis aset, merupakan masalah yang dihadapi dalam audit internal aset tetap. Dalam penelitian ini, data aset tetap dari suatu instansi dikelompokkan menggunakan teknik K-Means berdasarkan kesamaan atribut. Laporan keuangan internal tiga tahun sebelumnya menyediakan data tersebut. Temuan analisis menunjukkan bahwa kelompok aset dengan risiko penyusutan berlebih dan potensi kerugian aset yang tinggi dapat diidentifikasi menggunakan metode pengelompokan. Untuk menjamin kelangsungan operasional dan keakuratan laporan keuangan, aset tetap merupakan sumber daya krusial yang perlu dikelola dan dicatat dengan tepat. Pelaporan dan pencatatan aset tetap yang tidak akurat dapat memengaruhi pengambilan keputusan manajerial dan hasil audit internal. Melalui analisis berorientasi objek dan perancangan sistem informasi manajemen aset tetap, proyek ini bertujuan untuk meningkatkan efektivitas proses audit internal. Metode ini mencakup analisis kebutuhan sistem, perancangan model berbasis Unified Modeling Language (UML), dan implementasi awal melalui simulasi perancangan sistem. Auditor internal dapat lebih mudah mengikuti, melacak, dan memvalidasi setiap transaksi yang melibatkan aset tetap karena desain sistem ini yang lebih modular, adaptif, dan berorientasi objek. Untuk memfasilitasi audit yang lebih cepat dan tepat, studi ini menyarankan agar sistem informasi dikembangkan lebih lanjut hingga mencapai implementasi berbasis desktop atau web yang lengkap.
Co-Authors Agustina Simangunsong Ahmad Zein Bosker Sinaga Chindy L Ritonga Daulay, Suandi Ellisa Purba Eviyanti Br Barus Finna Maulidina Fiqri Irhami Flora Br Lumban Toruan Humala Simangunsong Humala Simangunsong Kicky Maulana Nuraisana Nuraisana Petti Indrayati Sijabat Rangga Pohan Refin Herissandi Rizki Fadila Nasution Satria Lutfi Maulana Selvi Yolanda Septian Jordan Purba Sinta Lia Alfaris Sulastri Sulastri Tyo Rizki Ramadhan Yogi Irwan Syahputra Yuda Perwira Yuni Endah Kurniati