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All Journal Sainteks Prosiding Semnastek Semesta Teknika JOURNAL OF SCIENCE AND SOCIAL RESEARCH Jurnal Teknologi Sistem Informasi dan Sistem Komputer TGD Jurnal SAINTIKOM (Jurnal Sains Manajemen Informatika dan Komputer) Jurnal Ekonomi Jutech: Jurnal Teknologi Informasi Jurnal Sistem Informasi Journal of Computer Science Advancements
Vany Terisia
Institut Teknologi Dan Bisnis Ahmad Dahlan, Jakarta
Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Engineering Mechanical Engineering Medicine & Pharmacology Social Sciences

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Rancangan Up-Link Point to Point Menggunakan Konfigurasi Load Balance and Failover Pada CV. Vyasti Network Syehand Aby Riano; R. Tommy Gumelar; Vany Terisia
Jurnal Teknologi Informasi (JUTECH) Vol 5 No 1 (2024): JUTECH: Jurnal Teknologi Informasi
Publisher : ITB Ahmad Dahlan Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32546/jutech.v5i1.2451

Abstract

CV Vyasti Network, a home internet service provider established in 2022, places the stability of internet connectivity as a top priority in its services. Maximum effort is made in the configuration of devices and systems to ensure a stable level of internet availability. In support of this goal, CV Vyasti Network adopts a design that involves the use of wireless back-up media and mixed routing protocol with NDLC method. The main purpose of this design is to minimize downtime when the main link experiences problems. To achieve this, load balancing is used so that downtime can be minimized when one of the links experiences interference. This method has proven to be very effective, especially in the results of its application on CV Fiber To The Home Vyasti.
Classification of Brain Image Tumor using EfficientNet B1-B2 Deep Learning Hastomo, Widi; Karno, Adhitio Satyo Bayangkari; Sestri, Ellya; Terisia, Vany; Yusuf, Diana; Arman, Shevty Arbekti; Arif, Dodi
Semesta Teknika Vol 27, No 1 (2024): MEI
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/st.v27i1.19691

Abstract

In this study, a new neural network model (EfficientNet B1-B2) was sought for the detection of brain tumors in magnetic resonance imaging (MRI) images. The primary objective was to achieve high accuracy rates so as to classify the images. The deep learning techniques meticulously processed and increased the data augmentation as much as possible for the EfficientNet B1-B2 models. Our experimental results show an accuracy of 98% in the B1 version in Table II. This provides a potentially optimistic view of the application of artificial intelligence technology to disease diagnosis based on medical image analysis. Nonetheless, we must remind ourselves that the dataset we used has limitations in terms of the challenges it can pose. Although the number of potential variations of actual medical images constitutes a major challenge, it is not the only one. Most medical datasets are unbalanced, contain highly variable noise, have a slow internal structure, and are often small in size. Hence, our end goal is to help stimulate not only the field of brain tumor detection and treatment but also the development of more sophisticated classification models in the health context.
Sistem Pakar Menggunakan Forward Chaining Untuk Memprediksi Gangguan Kejiwaan Vany Terisia; Akmal Annafis; Arman, Shevti Arbekti
Jurnal Teknologi Informasi (JUTECH) Vol 5 No 2 (2024): JUTECH: Jurnal Teknologi Informasi
Publisher : ITB Ahmad Dahlan Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32546/jutech.v5i2.2922

Abstract

Mental health is one of the most important aspects of human life, especially in the modern era, which is filled with stress and challenges. However, many individuals are reluctant or face difficulties in accessing mental health services due to social stigma and limited resources. To address this challenge, this study developed a web-based expert system to predict an individual's mental health condition using the Forward Chaining method. The Forward Chaining method is utilized to analyze symptoms inputted by users based on predefined rules, enabling the system to provide accurate initial diagnoses and appropriate recommendations. The application is designed to be user-friendly and easily accessible, assisting individuals in obtaining mental health information and support without fear of stigma. Additionally, the system also has the potential to serve as a supporting tool for mental health professionals in providing efficient initial diagnoses.
Sistem Pendukung Keputusan Pemberian Reward Karyawan Berdasarkan Kinerja Menggunakan Metode SAW Terisia, Vany; Arman, Shevti Arbekti; Diana Yusuf; Fahrul Razi
Jurnal Teknologi Informasi (JUTECH) Vol 6 No 1 (2025): JUTECH: Jurnal Teknologi Informasi
Publisher : ITB Ahmad Dahlan Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32546/jutech.v6i1.3113

Abstract

Rewarding employees is one of the important strategies for improving motivation and performance within an organization. However, many companies face difficulties in determining which employees deserve rewards in a fair and objective manner. This research aims to develop a Decision Support System (DSS) that can assist in determining which employees are entitled to receive rewards based on several relevant criteria, using the Simple Additive Weighting (SAW) method. The SAW method is chosen for its ability to solve decision-making problems that involve multiple criteria with multi-attribute characteristics. The criteria used in this study include performance, discipline, contribution to the team, and initiative. The results of the study indicate that the application of the SAW method can produce objective and transparent decisions, as well as provide a solid basis for rewarding employees. The employee alternative with the highest score based on the SAW calculation is selected as the reward recipient, which is expected to enhance employee motivation and performance within the company. This research is expected to contribute to the development of a more systematic and data-driven reward system.
Desain Komunikasi Visual Berbasis Segmentasi Pelanggan untuk H&M Terisia, Vany; Hastomo, Widi; Sestri, Elliya; Syamsu, Muhajir; Novitasari, Lyscha; Putra, Yoga Rarasto; Fiqhri, Zul; Sudarwanto, Pantja; Daruningsih, Kukuh
Prosiding Semnastek PROSIDING SEMNASTEK 2025
Publisher : Universitas Muhammadiyah Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Penelitian ini bertujuan untuk merancang strategi komunikasi visual berdasarkan segmentasi pelanggan pada industri fashion retail, studi kasus pada H&M Group. Data diambil dari dataset H&M Personalized Fashion Recommendations di Kaggle dan diolah dengan pendekatan RFM (Recency, Frequency, Monetary) serta algoritma K-Means clustering untuk mengidentifikasi tipe pelanggan. Hasil analisis menunjukkan tiga klaster utama: pelanggan bernilai tinggi, sedang, dan rendah. Berdasarkan hasil tersebut, dirancang pendekatan visual yang berbeda untuk setiap segmen, baik dalam desain iklan digital maupun visual merchandising. Penelitian ini memberikan kontribusi dalam pengambilan keputusan pemasaran visual yang berbasis data untuk meningkatkan retensi pelanggan.
Prediksi Risiko Stunting pada Balita menggunakan Algoritma Logistic Regression dan Decision Tree berbasis Data Terbuka Yusuf, Diana; Razi, Fahrul; Arman, Shevti Arbekti; Terisia, Vany; Nurjayanti , Revi
Prosiding Semnastek PROSIDING SEMNASTEK 2025
Publisher : Universitas Muhammadiyah Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Stunting masih menjadi tantangan kesehatan utama yang berdampak pada pertumbuhan dan perkembangan anak di Indonesia. Upaya deteksi dini risiko stunting memerlukan pendekatan berbasis data yang akurat dan praktis. Penelitian ini bertujuan mengembangkan dan membandingkan dua model klasifikasi, yaitu Logistic Regression dan Decision Tree, untuk memprediksi risiko stunting pada balita dengan memanfaatkan data kesehatan terbuka. Dataset yang digunakan mencakup variabel usia, berat lahir, berat badan, panjang badan, dan jenis kelamin. Proses penelitian meliputi preprocessing data, pemabagian data menjadi data latih dan data uji, penerapan model, serta evaluasi performa menggunakan metrik akurasi, precision, recall, dan F1-Score. Hasil penelitian menunjukkan bahwa Logistic Regression memberikan performa lebih stabil dengan akurasi 84,4% pada data latih dan uji. Decision Tree memiliki akurasi lebih tinggi pada data latih (96,5%) namun menurun pada data uji (78,7%), menunjukkan kecenderungan overfitting. Visualisasi Decision Tree mengungkapkan bahwa usia dan berat badan menjadi fitur paling dominan dalam klasifikasi risiko stunting. Berdasarkan hasil tersebut, Logistic Regression direkomendasikan sebagai model yang lebih andal untuk implementasi prediski stunting di tingkat layanan kesehatan masyarakat. Temuan ini diharapkan dapat memberikan kontribusi bagi pengembangan sistem pendukung keputusan berbasis data dalam mitigasi stunting.
Rekomendasi Karyawan Tetap Menggunakan Metode Weighted Product (WP) pada PT. KB Multifinance Terisia, Vany; Arman, Shevti Arbekti; Syamsu, Muhajir
Jurnal Teknologi Sistem Informasi dan Sistem Komputer TGD Vol. 7 No. 1 (2024): J-SISKO TECH EDISI JANUARI
Publisher : STMIK Triguna Dharma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53513/jsk.v7i1.9518

Abstract

Sumber Daya Manusia (SDM) memegang peranan krusial dalam mencapai tujuan perusahaan, karena kualitasnya sangat memengaruhi produktivitas dan kinerja organisasi. Potensi pegawai tercermin dalam pencapaian kerja, membandingkan hasil kerja dengan standar yang ditetapkan. Proses pemilihan calon karyawan tetap di PT. KB Multifinance, perusahaan pembiayaan multiguna, melibatkan evaluasi tahunan dengan tiga hasil rekomendasi: diangkat sebagai karyawan tetap, dikontrak kembali, atau diberhentikan. Penilaian kualitas karyawan tetap dilakukan berdasarkan Pencapaian Sasaran Kerja (KPI), Kompetensi Kerja, dan Kompetensi Perilaku (Behavioral Competencies). Namun, metode perhitungan matematis biasa yang digunakan saat ini menimbulkan kompleksitas dan memerlukan peningkatan efisiensi. Oleh karena itu, penelitian ini memfokuskan pada pembangunan sistem pendukung keputusan dengan menerapkan metode Weighted Product (WP). Metode ini menggunakan perkalian untuk menghubungkan nilai kriteria, dengan pembobotan pada setiap kriteria yang dipangkatkan. Tujuannya adalah meningkatkan objektivitas dan efektivitas dalam penilaian calon karyawan tetap di PT. KB Multifinance. Kesimpulan penelitian menunjukkan pengembangan sistem WP untuk identifikasi calon karyawan tetap dan hasil partisipasi calon yang dihitung secara efektif melalui langkah operasional sistem, mengurangi ketergantungan pada perkiraan subjektif.
Gas Store Data Analysis Using ERD Method and Constitutional Data Warehouse Model Risaldi, Fahmi; Terisia, Vany; Arman, Shevty Arbekti; Yusuf, Diana
Journal of Computer Science Advancements Vol. 1 No. 3 (2023)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/jsca.v1i3.540

Abstract

A data warehouse is a data storage system that plays a crucial role in business analysis. It collects, integrates, and stores data from multiple sources in a structured format, providing holistic insight into organizational performance. Entity-Relationship Model (ERD) is a visual tool for designing database structures. It uses entities to represent real-world objects and the relationships between them. ERD helps plan an efficient and coherent database design. A conceptual model is an abstract visual representation of information structures and relationships within a domain. It covers key concepts and business rules, assisting in building a solid foundation of understanding before technical designing begins. All three are interrelated in the development of successful information systems. Data warehouses use conceptual models to direct effective data storage design, while ERD helps describe the entities and relationships to be stored in the data warehouse. The combination of all three enables organizations to design, develop, and maintain adequate information systems, based on a deep understanding of data and its relationships. This results in better decision making, more efficient innovation, and optimal utilization of resources. The purpose of this study is to produce optimal data using the ERD method. The main objective is to explain how much data in an information system in the Company and how data management is crucial for effective decision making.
Co-Authors Adhitio Satyo Bayangkari Karno Akmal Annafis Cakranegara, Pandu Adi Daruningsih, Kukuh Diana Yusuf Diana Yusuf Dodi Arif Elliya Sestri Fahrul Razi Fahrul Razi Fauzan, Alif Raihanza Fiqhri, Zul Gumelar, R Tommy Indri, Indri Mulyani Karno, Adhitio Satyo Bayangkari Lyscha Novitasari Muhajir Syamsu Muhajir Syamsu Muhammad Ilham Pakawaru Nada Kamilia Nurjayanti , Revi Pradanni Kresna Mukti Prita Niken Puspita Putra, Yoga Rarasto Raihan, Dendi Reza Fitriansyah Risaldi, Fahmi Riska Sulistiani Sestri, Elliya Sestri, Ellya Shevti Arbekti Arman Silvia Ningsih Stefani Lily Indarto Sudarwanto, Pantja Syamsu, Muhajir Syehand Aby Riano Tanti Widia Nurdiani Toha, Muhamad Toha, Muhamad Toha Widi Hastomo YUSUF, DIANA