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All Journal Journal of Intelligent Systems Jurnal Pilar Nusa Mandiri Information System for Educators and Professionals : Journal of Information System Information Management For Educators And Professionals (IMBI) Jurnal Teknik Informatika STMIK Antar Bangsa JITK (Jurnal Ilmu Pengetahuan dan Komputer) Techno Nusa Mandiri : Journal of Computing and Information Technology Jurnal Nasional Komputasi dan Teknologi Informasi Jurnal Riset Informatika Budimas : Jurnal Pengabdian Masyarakat Jurnal Infortech International Journal Of Science, Technology & Management (IJSTM) Jurnal Sistem Informasi Jurnal AbdiMas Nusa Mandiri Jurnal Dialektika Informatika (Detika) Algoritme Jurnal Mahasiswa Teknik Informatika INFORMATION SYSTEM FOR EDUCATORS AND PROFESSIONALS : Journal of Information System Jurnal Pengabdian Kreatif Cemerlang Indonesia AMMA : Jurnal Pengabdian Masyarakat Jurnal Abdimastek (Pengabdian Masyarakat Berbasis Teknologi) Prawara Jurnal Pengabdian Masyarakat KOMPUTASI
Tyas Setiyorini
Universitas Nusa Mandiri
Religion Aerospace Engineering Agriculture, Biological Sciences & Forestry Humanities Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Neuroscience Nursing Physics Public Health Social Sciences Transportation Other

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Analisis Sentimen Ulasan Mobile Legend Menggunakan Algoritma Naive Bayes, SVM, Logistic Regression Alengka, Son Gohan; Putra, Jordy Lasmana; Setiyorini, Tyas
Algoritme Jurnal Mahasiswa Teknik Informatika Vol 6 No 1 (2025): Oktober 2025 || Algoritme Jurnal Mahasiswa Teknik Informatika
Publisher : Program Studi Teknik Informatika Universitas Multi Data Palembang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35957/algoritme.v5i3.12915

Abstract

The rapid growth of the mobile gaming industry in Indonesia, particularly Mobile Legends: Bang-Bang, has generated millions of user reviews on the Google Play Store, making manual analysis inefficient and prone to bias. This study compares three algorithms—Naive Bayes, Support Vector Machine (SVM), and Logistic Regression—for sentiment analysis of 52,651 reviews. Preprocessing includes text cleaning, stopword removal (Indonesian/English), Sastrawi stemming, and TF-IDF representation (min_df=3, max_df=0.9, n-gram 1–2). Binary labeling follows a rating-based approach: 1–2 stars (negative), 4–5 stars (positive), while 3-star reviews are excluded due to ambiguity. Evaluation using accuracy, precision, recall, F1-score, confusion matrix, and Cohen’s Kappa shows SVM and Logistic Regression achieving ≈90–91%, with SVM chosen as the default model for its balanced metrics and margin stability. The model can be deployed as an API service (Flask/FastAPI) for near real-time review monitoring (e.g., lag, AFK, matchmaking), enabling alert thresholds and improvement prioritization. Findings remain limited to Mobile Legends reviews on Google Play, requiring further validation across other applications.
Comparison of the Application of Neural Networks with K-Fold Cross Validation and Sliding Window Validation for Forecasting Covid-19 Recovered Cases Tyas Setiyorini
Jurnal Riset Informatika Vol. 6 No. 1 (2023): December 2023
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34288/jri.v6i1.263

Abstract

The Covid-19 virus first appeared in China resulting in millions of confirmed cases, deaths and recovered cases to date. The spread and increase in the death rate due to Covid-19 is very worrying. Health workers and researchers continue to struggle to improve recovery from Covid-19 cases. There is a need for future forecasting to predict recovery from cases that occur, so that the public or government can understand the spread, take precautions and prepare for action as early as possible. Several previous studies have carried out forecasting the future impact of Covid-19 using Machine Learning methods. Neural Network and Sliding Window are appropriate methods for forecasting time series data. In this research, it has been proven that the application of a Neural Network with a Sliding Window can improve performance which is much better than without using a Sliding Window in forecasting Covid-19 recovery cases in China.
Comparison of the Application of Linear Regression with Sliding Window Validation and K-Fold Cross-Validation for Forecasting Covid-19 Recovered Cases Setiyorini, Tyas; Frieyadie, Frieyadie
Jurnal Riset Informatika Vol. 6 No. 3 (2024): June 2024
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34288/jri.v6i3.288

Abstract

The increase in confirmed cases and deaths due to Covid-10 continues to spread and increase day by day throughout the world. This has resulted in a world health crisis that impacts all sectors of life. The government declared a movement to suppress the spread of Covid-19, so it is necessary to understand the pattern of Covid-19 problems. Researchers contribute scientifically to finding patterns of death or recovery due to COVID-19 by applying Machine Learning methods. The Linear Regression and Sliding Window preprocessing methods are appropriate for forecasting time series data. This research obtained RMSE results at 0.320 with linear regression with sliding window validation and RMSE at 0.320 with linear regression with K-Fold cross-validation. This proves that Linear Regression with Sliding Window Validation can improve performance much better than k-fold cross-validation in forecasting COVID-19 recovery cases in China. The sliding window validation method has been proven to increase accuracy for forecasting with time series data compared to other standard preprocessing methods, namely K-Fold cross-validation. In the future, further research is needed to test different types of time series data by comparing the application of sliding window validation and K-Fold cross-validation or developing other validation models.
Perancangan Sistem Manajemen Data Barang Berbasis Website Pada FC Computer PIK Jakarta Utara Fariq Mulia, Akbar; Jordy Lasmana Putra; Tyas Setiyorini
Jurnal Esensi Infokom : Jurnal Esensi Sistem Informasi dan Sistem Komputer Vol 9 No 2 (2025)
Publisher : Institut Bisnis Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55886/infokom.v9i2.331

Abstract

FC Computer PIK Jakarta Utara merupakan toko yang bergerak di bidang penjualan dan layanan jasa teknologi, yang masih menggunakan sistem semi-manual berbasis Excel dan Dropbox dalam pengelolaan stok barang. Sistem tersebut menimbulkan sejumlah kendala, seperti konflik file, potensi human error, keterbatasan kolaborasi, dan tingkat keamanan yang rendah. Penelitian ini bertujuan merancang sistem manajemen data barang berbasis website guna meningkatkan efisiensi, akurasi, dan keamanan dalam pengelolaan stok. Metode yang digunakan adalah Waterfall, dengan tahapan analisis kebutuhan, perancangan, implementasi, hingga pengujian sistem. Hasilnya adalah sebuah aplikasi berbasis web yang memungkinkan pencatatan barang masuk dan keluar secara real-time, pengelolaan data pengguna, pembuatan laporan, serta fitur login dengan hak akses berbeda antara admin dan owner. Sistem ini dibangun menggunakan PHP dan MySQL dan telah diuji menggunakan metode blackbox, yang menunjukkan bahwa seluruh fungsi utama berjalan sesuai dengan kebutuhan. Sistem ini diharapkan mampu menjadi solusi efektif dalam mengatasi permasalahan stok barang di FC Computer PIK, sekaligus mendukung pengambilan keputusan yang lebih baik. Kata Kunci: Website, Waterfall, PHP, MySQL, FC Computer PIK.
KLASTERISASI DATA MINING PENCARI KERJA DKI JAKARTA MENGGUNAKAN METODE K-MEANS CLUSTERING lazuardi, sandy ibrahim; Putra, Jordy Lasmana; Setiyorini, Tyas
Jurnal Dialektika Informatika (Detika) Vol 6, No 1 (2025): Jurnal Dialektika Informatika(Detika) Vol.6 No.1 Desember 2025
Publisher : Universitas Muria Kudus

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24176/detika.v6i1.15471

Abstract

Tingkat pengangguran di DKI Jakarta yang masih tinggi menjadi permasalahan serius yang memerlukan solusi berbasis data. Salah satu faktor utama yang memengaruhi tingkat pengangguran adalah pendidikan, sehingga analisis karakteristik pencari kerja sangat penting untuk mendukung kebijakan ketenagakerjaan. Penelitian ini bertujuan untuk mengelompokkan data pencari kerja di DKI Jakarta berdasarkan tingkat pendidikan dan jenis kelamin menggunakan metode K-Means Clustering. Data yang digunakan diperoleh dari Satu Data Jakarta periode 2022 hingga 2024. Proses penelitian meliputi pengumpulan data, pembersihan dan transformasi data, penentuan jumlah klaster optimal dengan metode Elbow, serta implementasi algoritma K-Means menggunakan perangkat lunak RapidMiner. Evaluasi hasil klasterisasi dilakukan menggunakan Davies-Bouldin Index (DBI), di mana nilai DBI terbaik yang diperoleh adalah -0,920, menandakan kualitas klaster yang baik dan kompak. Hasil penelitian menunjukkan bahwa pencari kerja dengan pendidikan universitas mendominasi kelompok terbesar pada tahun 2023. Temuan ini diharapkan dapat membantu pemerintah dan lembaga terkait dalam merancang program pelatihan dan penyaluran tenaga kerja yang lebih efektif dan tepat sasaran di DKI Jakarta.
Optimalisasi Pengelolaan Tagihan Melalui Sistem Notifikasi WhatsApp Berbasis Framework Laravel dan Metode Rapid Application Development Harsih Rianto; Tyas Setiyorini
Jurnal Nasional Komputasi dan Teknologi Informasi (JNKTI) Vol 7, No 6 (2024): Desember 2024
Publisher : Program Studi Teknik Komputer, Fakultas Teknik. Universitas Serambi Mekkah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32672/jnkti.v7i6.8391

Abstract

Abstrak - Pengelolaan tagihan atau invoice yang tidak efisien dapat menimbulkan dampak serius bagi perusahaan, seperti keterlambatan penerimaan pembayaran, konflik dengan pelanggan, dan kerugian finansial. Untuk mengatasi masalah ini, sistem pengelolaan tagihan berbasis web yang dilengkapi dengan notifikasi otomatis menjadi solusi yang relevan. Salah satu media komunikasi yang efektif untuk notifikasi tagihan adalah WhatsApp, karena keunggulannya dalam aksesibilitas, kecepatan, dan kemampuan untuk menyampaikan informasi secara interaktif. Dalam penelitian ini, pengembangan sistem dilakukan menggunakan framework Laravel 10, yang menawarkan fleksibilitas, dokumentasi lengkap, dan fitur bawaan untuk mempermudah proses pengembangan. Selain itu, metode Rapid Application Development (RAD) digunakan untuk mempercepat proses pembangunan sistem dengan pendekatan iteratif dan umpan balik pengguna yang berkelanjutan. Sistem ini dirancang untuk meningkatkan efisiensi pengelolaan tagihan, memastikan pelanggan menerima informasi dengan cepat, dan meminimalkan kesalahan dalam pencatatan. Hasil pengembangan diharapkan dapat memberikan solusi inovatif bagi perusahaan dalam mengelola tagihan secara lebih efektif, meningkatkan arus kas, dan membangun hubungan yang lebih baik dengan pelanggan. Studi ini juga mengisi gap penelitian terkait pengelolaan tagihan otomatis berbasis WhatsApp dengan menggunakan Laravel dan metode RAD.Kata kunci: Pengelolaan Tagihan, Notifikasi Tagihan, WhatsApp, Laravel, Rapid Application Development (RAD) Abstract - Inefficient management of invoices can cause significant issues for companies, including delayed payments, customer conflicts, and financial losses. To address these challenges, a web-based invoice management system equipped with automated notifications offers a relevant solution. WhatsApp emerges as an effective medium for invoice notifications due to its accessibility, speed, and ability to deliver interactive information. This study develops a system using the Laravel 10 framework, which provides flexibility, comprehensive documentation, and built-in features that simplify the development process. Additionally, the Rapid Application Development (RAD) method is applied to accelerate system creation through iterative approaches and continuous user feedback. The system is designed to enhance invoice management efficiency, ensure timely delivery of information to customers, and minimize recording errors. The development outcome aims to provide innovative solutions for companies to manage invoices more effectively, improve cash flow, and foster better customer relationships. This study also addresses a research gap in developing automated invoice management systems via WhatsApp using Laravel and the RAD methodology.Keywords: Invoice Management, Invoice Notification, WhatsApp, Laravel, Rapid Application Development (RAD)
Decision Support System for Cloud Computing Service Selection Using the Weighted Product Method (Case Study: PT. Deptech Digital Indonesia) saputra, dedi; Kudiantoro Widianto; Tyas Setiyorini; Ibnu Alfarobi
International Journal of Science, Technology & Management Vol. 2 No. 1 (2021): January 2021
Publisher : Publisher Cv. Inara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46729/ijstm.v2i1.103

Abstract

The selection of cloud computing services requires careful consideration and review. Some aspects of the criteria that must be considered such as direct selection between services that take a long time and need to be done repeatedly. Decision Support System with Weighted Product (WP) method is an effective method because the time needed for calculation is much shorter. The purpose of this research is to apply Weighted Product (WP) method in the decision support system to choose cloud computing services where as a case study is PT Deptech Digital Indonesia, so that it can make it easier for companies to make decisions according to their needs. The calculation results using the WP method give preference to the top 3 services: Google Cloud, Amazon Web Services and Microsoft Azure. This proves that research with the WP method can be applied to various services that will be used in the future according to predetermined criteria. Based on these results, the system can recommend cloud computing services according to the needs and a good level of accuracy.
VILLAGE GROUPING BASED ON THE NUMBER OF HEALTH FACILITIES IN WEST JAVA USING K-MEANS CLUSTERING ALGORITHM Frieyadie, Frieyadie; Andriansyah, Anggie; Setiyorini, Tyas
Jurnal Riset Informatika Vol. 4 No. 1 (2021): December 2021
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34288/jri.v4i1.140

Abstract

Health is very important for the welfare and development of the Indonesian nation because as a capital for the implementation of national development, it is essentially the development of all Indonesian people and the development of all Indonesian people. Due to the outbreak of the Covid-19 virus, many health facilities must be provided for patients. Of course, the government must pay attention to the health facilities that can be used in every district/city in West Java in the future. Therefore, to determine the level of availability of sanitation facilities in each district/city in West Java, we need a technology that can classify data correctly. One method of data processing in data mining is clustering. The application of clustering to this problem can use the K-Means algorithm method to group the most frequently used data. The purpose of this study is to classify sanitation data on the highest sanitation facilities, medium sanitation facilities, and low sanitation facilities, so that areas/cities that are included in the low cluster will receive more attention from the government to improve/provide sanitation facilities.
COMPARISON OF LINEAR REGRESSIONS AND NEURAL NETWORKS FOR FORECASTING COVID-19 RECOVERED CASES Setiyorini, Tyas; Frieyadie, Frieyadie
Jurnal Riset Informatika Vol. 4 No. 3 (2022): June 2022
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34288/jri.v4i3.184

Abstract

The emergence of the Covid-19 outbreak for the first time in China killed thousands to millions of people. Since the beginning of its emergence, the number of cases of Covid-19 has continued to increase until now. The increase in Covid-19 cases has a very bad impact on health and social and economic life. The need for future forecasting to predict the number of deaths and recoveries from cases that occur so that the government and the public can understand the spread, prevent and plan actions as early as possible. Several previous studies have forecast the future impact of Covid-19 using the Machine Learning method. Time series forecasting uses traditional methods with Linear Regression or Artificial Intelligence methods with neural networks. The research proves a linear relationship in the time series data of Covid-19 recovered cases in China, so it is proven that Linear Regression performance is better than the Neural Network.
KLASTERISASI DATA MINING PENCARI KERJA DKI JAKARTA MENGGUNAKAN METODE K-MEANS CLUSTERING lazuardi, sandy ibrahim; Putra, Jordy Lasmana; Setiyorini, Tyas
Jurnal Dialektika Informatika (Detika) Vol. 6 No. 1 (2025): Jurnal Dialektika Informatika(Detika) Vol.6 No.1 Desember 2025
Publisher : Universitas Muria Kudus

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24176/detika.v6i1.15471

Abstract

Tingkat pengangguran di DKI Jakarta yang masih tinggi menjadi permasalahan serius yang memerlukan solusi berbasis data. Salah satu faktor utama yang memengaruhi tingkat pengangguran adalah pendidikan, sehingga analisis karakteristik pencari kerja sangat penting untuk mendukung kebijakan ketenagakerjaan. Penelitian ini bertujuan untuk mengelompokkan data pencari kerja di DKI Jakarta berdasarkan tingkat pendidikan dan jenis kelamin menggunakan metode K-Means Clustering. Data yang digunakan diperoleh dari Satu Data Jakarta periode 2022 hingga 2024. Proses penelitian meliputi pengumpulan data, pembersihan dan transformasi data, penentuan jumlah klaster optimal dengan metode Elbow, serta implementasi algoritma K-Means menggunakan perangkat lunak RapidMiner. Evaluasi hasil klasterisasi dilakukan menggunakan Davies-Bouldin Index (DBI), di mana nilai DBI terbaik yang diperoleh adalah -0,920, menandakan kualitas klaster yang baik dan kompak. Hasil penelitian menunjukkan bahwa pencari kerja dengan pendidikan universitas mendominasi kelompok terbesar pada tahun 2023. Temuan ini diharapkan dapat membantu pemerintah dan lembaga terkait dalam merancang program pelatihan dan penyaluran tenaga kerja yang lebih efektif dan tepat sasaran di DKI Jakarta.
Co-Authors ACHMAD RIFAI Achmad Rifai Aditiya Yoga Pratama Albert Riyandi Alengka, Son Gohan Andriansyah, Anggie Angga Subrata Anggie Andriansyah Dedi Saputra Fariq Mulia, Akbar Fitra Septia Nugraha Frieyadie Frieyadie Frieyadie Harsih Rianto Ibnu Alfarobi Ibnu Rusdi Istiyanto, Ilham Jordy Lasmana Putra Kudiantoro Widianto, Kudiantoro lazuardi, sandy ibrahim Maryanah Safitri Maryanah Safitri Muhamad Ryansyah Nofarenzi, Yosari Nur Mutia Eka Pusparani Nurajijah Rismaniah Rismaniah Rizky Tri Asmono Rizky Tri Asmono Rizky Tri Asmono Rizky Tri Asmono Rizky Tri Asmono Romi Satria Wahono Sita Anggraeni Sita Anggraeni, Sita Siti Nurdiani Solehudin, Rizki Sri Syaifur Rahmatullah Syaifur Rahmatullah, Syaifur