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All Journal INFOSYS (INFORMATION SYSTEM) JOURNAL Jurnal Komputer Teknologi Informasi Sistem Komputer (JUKTISI) PUBLIDIMAS CSRID
Wahyu Saptha Negoro
Unknown Affiliation
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice

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Penerapan Knowledge Management System Sentra Pelayanan Kepolisian Terpadu Pada Polsek Medan Barat Berbasis Web Diki Ramanda; Edy Victor Haryanto; Rini Oktari Batubara; Wahyu Saptha Negoro
INFOSYS (INFORMATION SYSTEM) JOURNAL Vol 7, No 2 (2023): InfoSys Februari 2023
Publisher : Universitas Potensi Utama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22303/infosys.7.2.2023.212 - 223

Abstract

Polsek Medan Barat merupakan salah satu pusat pelayanan masyarakat yang berada di wilayah  Medan Barat. Kepolisian di bidang SPKT (Sentra Pelayanan Kepolisian Terpadu) memberikan pelayanan kepolisian terpadu, bantuan dan pertolongan, serta pelayanan informasi pelaporan dan pengaduan di masyarakat. Dalam hal ini, petugas SPKT harus cepat tanggap terhadap laporan masyarakat yang terima. Tujuan penelitian ini adalah untuk membangun sistem Aplikasi KMS diharapkan dapat membantu Pihak Administrasi SPKT dengan layanan pengaduan kepada masyarakat, dan sistem yang mampu menyebarluaskan informasi knowledge secara online atau cepat tanggap. Disamping itu, kurangnya pengetahuan masyarakat terkait tata cara melakukan pengaduan atau keluhan seperti pengaduan tindak pidana kriminal yang di dalamnya termasuk laporan penipuan, perampokan, penggelapan, pencurian, kehilangan barang berharga dan sebagainya. Aplikasi Knowledge Management System (KMS) akan dirancang berbasis website dengan menggunakan bahasa pemrograman PHP dan MYSQL sebagai basis datanya. Adapun sistem yang akan dibangun diharapkan akan menjadi wadah Sharing knowledge kepada masyarakat dalam mengakses pelayanan informasi dokumen dari sentra pelayanan kepolisian terpadu dan terciptanya forum diskusi.
Literasi Informasi Berbasis Search Engine di SMK PAB 8 Sampali: Literasi Informasi Berbasis Search Engine di SMK PAB 8 Sampali Wahyu Saptha Negoro; idzhari rahman; Arbana Syamanta
Publikasi Pengabdian Masyarakat Vol 3 No 1 (2023): PUBLIDIMAS Vol. 3 No. 1 MEI 2023
Publisher : LPPM Universitas Potensi Utama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22303/publidimas.v3i1.169

Abstract

Since its birth in the 1960s, the Internet has become an integral part of modern and dynamic society. Several search engines provide a set of tools which, when further explored, will be very useful in shortening search time. The tool includes search techniques using several commands that must be entered into the query by the user. It has been discussed before that there are many search engines (search engines) that can be used to search for information on the internet. However, with all these search engines, Google is still the best. Google is one of the giant companies on the internet. Google has proven to have made a very big contribution to the world community, especially in accessing global information. Advances in science and information technology in the form of internal impact on all aspects of human life. The internet has had a huge impact by penetrating various people's lives and behavior. Keywords : Information Search and Seacrch Engine
JARINGAN SYARAF TIRUAN DENGAN LEARNING VECTOR QUANTIZATION (LVQ) UNTUK KLASIFIKASI DAUN: ARTIFICIAL NEURAL NETWORKS USING LEARNING VECTOR QUANTIZATION (LVQ) FOR LEAF CLASSIFICATION Soeheri; Sari, Rita; Wahyu Saptha Negoro; Yuhandri
CSRID (Computer Science Research and Its Development Journal) Vol. 16 No. 1 (2024): February 2024
Publisher : LPPM Universitas Potensi Utama

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Abstract

Leaves are one part of a plant species that is commonly used to classify plant and plant species. The process of assisting various types of leaves usually involves experts using a herbarium, which is a collection of preserved plant specimens. Leaf classification is the detection of different types of leaves, where there are 2 types of leaves including Magnolia Soulangeana and Invillea leaves. The training data contains 30 images consisting of 15 each of the 2 types of leaves, then the test data contains 20 images which are also taken from the 2 types of leaves. So that the total images used are 50 leaf images. The leaf classification uses feature extraction and the method used in the classifier is Learning Vector Quantization (LVQ) which is a pattern classification method in which each output unit represents a particular class or group. The test results showed that the process of calling Magnolia Soulangeana and Bougainvillea leaves in this experiment was successful with 80% detection Keywords—Leaf classification, Learning Vector Quantization, Artificial Neural Networks, Feature extraction.
Classification and Interpretability of Employee Burnout Using Linear Discriminant Analysis Rochmawati, Dwi Robiul; Muhammad Al Adib; Diyo Mollana Fazri; Bill Raj; Romi Antoni; Rahmad Santoso; Wahyu Saptha Negoro
Jurnal Komputer Teknologi Informasi Sistem Komputer (JUKTISI) Vol. 4 No. 3 (2026): Februari 2026
Publisher : LKP KARYA PRIMA KURSUS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62712/juktisi.v4i3.811

Abstract

Employee burnout has become a critical challenge in modern organizations due to its negative impact on employees’ mental well-being, work performance, and organizational sustainability. In many workplaces, burnout identification still relies on subjective assessments and retrospective surveys, limiting the effectiveness of early intervention strategies. This study aims to develop an employee burnout risk classification model that achieves high predictive performance while maintaining strong interpretability. Linear Discriminant Analysis (LDA) is employed as the primary method because of its ability to separate classes optimally and provide explicit discriminant coefficients for explanatory analysis. The study utilizes a secondary dataset from the Mental Health in Workplace Survey, consisting of 3,000 employee records and 15 variables related to job characteristics, psychosocial factors, and individual conditions. The dataset is divided into training and testing sets with an 80:20 ratio. Experimental results show that the LDA model achieves an accuracy of 96.17%, with a precision of 89.50%, recall of 100%, F1-score of 94.46%, and an AUC value of 0.9988, indicating excellent classification capability. Further analysis of discriminant coefficients reveals that individual burnout indicators, job roles, work–life balance, and career growth opportunities are the most influential factors in determining burnout risk. These findings demonstrate that LDA offers an effective and interpretable approach for early burnout detection and supports evidence-based decision-making for human resource management.
Co-Authors Andri Armaginda Siregar Arbana Syamanta Bill Raj Diki Ramanda Diyo Mollana Fazri Dwi Robiul Rochmawati Edy Victor Haryanto Idzhari Rahman Jalaluddin Nasution Mhd. Agung Irnanda Muhammad Al Adib Rahmad Santoso Rahmat Humala Putra Hasibuan Rini Oktari Batubara Rita Sari Romi Antoni Soeheri Susiana Khosasih Yuhandri