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All Journal Jurnal Buana Informatika Jurnal Transformatika Jurnal Edukasi dan Penelitian Informatika (JEPIN) Sistemasi: Jurnal Sistem Informasi JOIV : International Journal on Informatics Visualization Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Journal of Information Technology and Computer Science (JOINTECS) JURNAL MEDIA INFORMATIKA BUDIDARMA INOVTEK Polbeng - Seri Informatika Indonesian Journal of Information System JTAM (Jurnal Teori dan Aplikasi Matematika) JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI Sebatik Indonesian Journal of Computing and Modeling Jurnal Informatika JUSIM (Jurnal Sistem Informasi Musirawas) Jutisi: Jurnal Ilmiah Teknik Informatika dan Sistem Informasi Journal of Information Systems and Informatics Aiti: Jurnal Teknologi Informasi Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika) Jurnal Teknik Informatika (JUTIF) Jurnal Indonesia : Manajemen Informatika dan Komunikasi JUSTIN (Jurnal Sistem dan Teknologi Informasi) Jurnal Ilmiah Sains Jurnal Ilmiah Ilmu Terapan Universitas Jambi Magistrorum et Scholarium: Jurnal Pengabdian Masyarakat IT-Explore: Jurnal Penerapan Teknologi Informasi dan Komunikasi Midang Jurnal Pendidikan Teknologi Informasi (JUKANTI) Jurnal Indonesia : Manajemen Informatika dan Komunikasi d'Cartesian: Jurnal Matematika dan Aplikasi Indonesian Journal of Intelligence Data Science (IJIDS) International Journal of Information Technology and Business JuTISI (Jurnal Teknik Informatika dan Sistem Informasi) Jurnal DIMASTIK
Yessica Nataliani
Universitas Kristen Satya Wacana
Religion Agriculture, Biological Sciences & Forestry Humanities Biochemistry, Genetics & Molecular Biology Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Physics Social Sciences Other

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FUN LEARNING WITH COMPUTER BAGI SISWA TK KRISTEN 1 SATYA WACANA SALATIGA Hermawan, Anton; Nataliani, Yessica; Dwi Purnomo, Hindriyanto; Christianto, Erwien; Setyanti, Angela Atik; Yulia, Hanita; Krismiyati, Krismiyati; Juliastomo Gundo, Adriyanto; Bayangkariwati Tacoh, Yuliana Tien; Wellem, Theophilus; Hendry, Hendry; Chandra, Dian W.
Midang Vol 3 No 3 (2025): Midang, Oktober 2025
Publisher : Unpad Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24198/midang.v3i3.63906

Abstract

ABSTRACT. The rapid development of information technology necessitates adjustments across various sectors, including education, particularly in early childhood education. Introducing technology at an early age is considered a strategic step in preparing children to face the challenges of the digital era. This community service program aimed to introduce technology, specifically computers, to kindergarten students. The program was in-person for students aged 4 to 5 years at TK Kristen 1 Satya Wacana, Salatiga. The training was conducted over multiple sessions, starting with an introduction to computer hardware, then developing mouse-handling skills, and engaging in interactive learning activities through educational games that addressed subjects like letters, numbers, geometric shapes, and different types of vehicles. Evaluation results revealed that the participants showed high enthusiasm throughout the sessions. The students also began to recognize computers as an alternative learning medium and other digital devices commonly used at home, such as smartphones and tablets. These findings suggest that a digital game-based learning approach can effectively enhance both students’ learning motivation and basic computer operation skills at an early age. Keywords: childhood education, computer training, education game, fun learning, gamebased learning.
Customer Loyalty Analysis Using RFM Model and K-Means Clustering for Marketing Strategy Optimization Vigo Yano Sahertian; Yessica Nataliani
International Journal of Information Technology and Business Vol. 8 No. 1 (2025): November : International Journal of Information Techonology and Business
Publisher : Universitas Kristen Satya Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24246/ijiteb.212025.01-07

Abstract

This study aims to segment customers to measure their level of loyalty using the RFM (Recency, Frequency, Monetary) model approach combined with the k-Means clustering algorithm. The dataset used comes from the Kaggle site and contains motor vehicle sales data, both cars and motorbikes, with a total of 2,747 transactions. The RFM method is used to calculate three important indicators of customer behavior, namely the last time to make a purchase (recency), purchase frequency (frequency), and total transaction value (monetary). The data is then normalized and grouped using the k-Means algorithm. Based on the results of the Elbow Method and Silhouette Score tests, the optimal number of clusters obtained is four. The segmentation results show four groups of customers with different characteristics, ranging from very loyal customers with high frequency and large transaction values, to customers who have been inactive for a long time. This segmentation is very useful for companies to design more targeted marketing strategies and increase customer retention. This study shows that the combination of RFM and k-Means clustering is able to provide significant insights in understanding consumer behavior and supporting data-based strategic decision making.
Prediksi Pergerakan Harga Saham Bank Mandiri Menggunakan Metode Support Vector Regression dan Algoritma Grid Search Francesco Totti Samuelly; Yessica Nataliani
Jutisi : Jurnal Ilmiah Teknik Informatika dan Sistem Informasi Vol 14, No 3: Desember 2025
Publisher : STMIK Banjarbaru

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35889/jutisi.v14i3.3206

Abstract

The volatile nature of stock price movements poses a major challenge for investors in making accurate investment decisions. This study aims to predict the stock price movement of PT. Bank Mandiri (Persero) Tbk [BMRI] using Support Vector Regression (SVR) optimized through the Grid Search algorithm. The dataset consists of daily stock prices from August 2020 to August 2025, including open, high, low, close, adjusted close, and trading volume. The research process involves data collection, preprocessing (cleaning, feature selection, normalization), splitting into training and testing sets, parameter optimization using Grid Search with Leave-One-Out Cross Validation (LOOCV), model training, and evaluation with R², MSE, and RMSE. The results show that the SVR model with a linear kernel, C = 1 and epsilon = 0.01, achieved the best performance, with high accuracy (R² = 0.9991 on training data and R² = 0.9976 on testing data). These findings confirm the effectiveness of Grid Search–based SVR in predicting stock prices and supporting investment decision-making.Keywords: Stock Price Prediction; Support Vector Regression; Grid Search; Bank Mandiri AbstrakPergerakan harga saham yang fluktuatif menjadi tantangan utama bagi investor dalam menentukan strategi investasi yang tepat. Penelitian ini bertujuan memprediksi pergerakan harga saham PT. Bank Mandiri (Persero) Tbk [BMRI] dengan metode Support Vector Regression (SVR) yang dioptimalkan menggunakan algoritma Grid Search. Data yang digunakan berupa harga saham harian periode Agustus 2020–Agustus 2025, mencakup variabel open, high, low, close, adjusted close, dan volume. Tahapan penelitian meliputi pengumpulan data, pra-pemrosesan (pembersihan, seleksi fitur, normalisasi), pembagian data latih dan uji, optimasi parameter dengan Grid Search berbasis Leave-One-Out Cross Validation (LOOCV), pelatihan model, serta evaluasi dengan R², MSE, dan RMSE. Hasil penelitian menunjukkan SVR dengan kernel linear, parameter C = 1 dan epsilon = 0,01 memberikan performa terbaik dengan akurasi tinggi (R² = 0,9991 pada data latih dan R² = 0,9976 pada data uji). Temuan ini menegaskan efektivitas SVR berbasis Grid Search dalam memprediksi harga saham dan mendukung pengambilan keputusan investasi. 
Prediksi Keberhasilan Studi Mahasiswa Menggunakan Metode Iterative Dichotomiser 3 Ester Gea; Yessica Nataliani
Jutisi : Jurnal Ilmiah Teknik Informatika dan Sistem Informasi Vol 14, No 3: Desember 2025
Publisher : STMIK Banjarbaru

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35889/jutisi.v14i3.3146

Abstract

Student academic success is an important indicator in evaluating the quality of higher education in Indonesia. This study aims to predict student academic success based on several variables, such as second-year GPA, number of credits passed, and student's province of origin, using the Iterative Dichotomiser 3 (ID3) algorithm. The data used were 650 graduates of the Information Systems Department of the Faculty of Information Technology, Satya Wacana Christian University, from 2013 to 2017. The ID3 classification results indicate that second-year GPA is the most significant attribute in determining academic success, while province of origin also influences the likelihood of students graduating or failing. Model evaluation using a confusion matrix yielded an accuracy of 85.38%, a precision of 88.8%, a recall of 89.8%, and F-measure of 89.3. These findings demonstrate that the ID3 method can be used in predicting student academic success.Keywords: Academic success; Student; Prediction; Iterative Dichotomiser 3 AbstrakKeberhasilan studi mahasiswa merupakan indikator penting dalam mengevaluasi kualitas pendidikan perguruan tinggi di Indonesia. Penelitian ini bertujuan untuk memprediksi keberhasilan studi mahasiswa berdasarkan sejumlah variabel seperti IPK tahun kedua, jumlah SKS lulus, dan provinsi asal mahasiswa dengan algoritma Iterative Dichotomiser 3 (ID3). Data yang digunakan berasal dari lulusan Fakultas Teknologi Informasi Program Studi Sistem Informasi Universitas Kristen Satya Wacana (UKSW) tahun 2013–2017 sebanyak 650 mahasiswa. Hasil klasifikasi ID3 menunjukkan bahwa IPK tahun kedua merupakan atribut paling signifikan dalam menentukan keberhasilan studi, sedangkan provinsi asal juga berpengaruh terhadap kecenderungan kelulusan atau kegagalan mahasiswa. Evaluasi model dilakukan menggunakan confusion matrix, hasilnya menunjukkan akurasi sebesar 85.38%, precision sebesar 88.8%, recall sebesar 89.8% dan F-measure sebesar 89.3%. Temuan ini menunjukkan bahwa metode ID3 dapat digunakan dalam memprediksi keberhasilan studi mahasiswa. 
Co-Authors Ade Iriani Adriyanto Juliastomo Gundo Advensius Natalis Agustinus Fritz Wijaya Alberaldo Difra Gunawan Aldi Lasso Alexander Franklyn Alwin Adi Putra Andree Eka Putra Anissa Enggar Pramitasari Anton Hermawan Anton Hermawan Antoni Erga Arthur, Christian Atik Setyanti, Angela Axsana, Samuel Richard Bayangkariwati Tacoh, Yuliana Tien Bryan Adha Elang Praditya Cahyaningtyas, Christian Chandra, Dian W. Chrisanty Mariana Rorimpandey Daniel D. Kameo Danny Manongga Darmawan Utomo Deinard Yordan Sihombing Diwa Oktario Dacwanda Edi Suharyadi Eko Sediyono Elizabeth Sri Lestari Erdi Amos Saputra Ering, Anatasya Lingkanwene Erwien Christianto Ester Gea Evi Maria Francesco Totti Samuelly Gerian, Matthew Gigih Prima Subakti Gilang Jonathan Phita Gregorry, Febrianus Hanita Yulia Hapsari, Theresia Shinta Hendry Hindriyanto Dwi Purnomo Hui-Ming Wee Imanuel Susanto Indrastanti Ratna Widiasari Irwan Sembiring Ivanna K. Timotius Jessica Widyadhana Iskandar Juliastomo Gundo, Adriyanto Kirono, Aryo Sasi Krismiyati Kristoko Dwi Hartomo Leony Martiyana Putri Lorna Yertas Baisa Marchelino Nathanael Maria Feby Tri Martin Martin Merryana Lestari Michael Jonathan Mramra , Welianus Yohanes Yehuda Mulyono, Andronikus Natalis, Advensius Penidas Fodinggo Tanaem Prandiska, Kelvin Putra, Andree Eka Rama Tri Budi Ardianto Saekoko, Agatha Marilin Samuel Wijayadi Sugiharto Sanjaya, Rian Sediatmoko, Nur Siradj Setyanti, Angela Atik Sugiarto, Adrian Herma Suharyadi Suryady, Irwan Tambunan, Shanto Moyrano Theophilus Wellem Theophilus Wellem Theopillus J. H. Wellem Tigar Cahyo Wiguno Tintien Koerniawati Vigo Yano Sahertian Wahab, Nur Haliza Abdul Willy Thomas Winsy C.D Weku Yuda Novianto