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All Journal Jurnal Buana Informatika Jurnal Teknologi Informasi dan Ilmu Komputer Jurnal Edukasi dan Penelitian Informatika (JEPIN) JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Jutisi: Jurnal Ilmiah Teknik Informatika dan Sistem Informasi bit-Tech INFORMASI (Jurnal Informatika dan Sistem Informasi) Informatics and Digital Expert (INDEX) Journal of Applied Computer Science and Technology (JACOST) Indonesian Journal of Digital Business MDP Student Conference jurnal informasi teknologi dan sistem Jurnal Informasi Teknologi dan Sistem (INTEKSIS)
Jimmy Tjen
Universitas Widya Dharma Pontianak
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Library & Information Science Other

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Distance Correlation-Based Regression Tree Algorithm For Structural Damage Detection Jimmy Tjen; Genrawan Hoendarto; Tony Darmanto; Thommy Willay
JATISI (Jurnal Teknik Informatika dan Sistem Informasi) Vol 10 No 2 (2023): JATISI (Jurnal Teknik Informatika dan Sistem Informasi)
Publisher : Lembaga Penelitian dan Pengabdian pada Masyarakat (LPPM) STMIK Global Informatika MDP

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35957/jatisi.v10i2.5402

Abstract

This paper proposes a novel idea of a fault detection algorithm based on the Regression Tree (RT) algorithm from the decision tree learning and the distance correlation, which is the nonlinear version of Pearson’s correlation, to reduce the number of sensors without significantly decreasing the model predictive accuracy and the fault diagnosis capability. A numerical validation on an experimental dataset provided by the Los Alamos National Laboratory (LANL) with MATLAB software shows that the proposed algorithm has a comparable model predictive accuracy to the classical RT while requiring a smaller number of sensors (5 instead of 24) and more robust in detecting faults with false negative and positive rates < 15%. Furthermore, we demonstrate that our proposed algorithm runs about 4 times faster than the classical RT on an experimental dataset with 4096 samples on an 8-core, 16 GB RAM machine. In a real-life setup, the proposed algorithm can be used to provide a sensor installment plan on a structure. Such that, the user can still monitor the presence of a fault inside a building precisely, but with a cheaper maintenance cost.
Algoritma Pendeteksi Kerusakan Struktur Bangunan Berbasis Korelasi Jarak dan Metode Kuadrat Terkecil Parsial Jimmy Tjen
JEPIN (Jurnal Edukasi dan Penelitian Informatika) Vol 8, No 3 (2022): Volume 8 No 3
Publisher : Program Studi Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26418/jp.v8i3.56011

Abstract

Sering kali, sebuah kerusakan struktur yang masif terjadi karena pengabaian terhadap kerusakan kecil. Kejadian malang ini kemudian menimbulkan berbagai kerugiaan, baik secara material maupun korban jiwa. Oleh karena itu, dirasa penting untuk dapat mendeteksi kerusakan dari sebuah struktur sedini mungkin untuk mencegah terjadinya hal yang tidak diinginkan. Penelitian ini menggagas sebuah algoritma pendektesi kerusakan struktur bangunan berbasiskan pada metode korelasi jarak dan kuadrat terkecil parsial. algoritma ini berfokuskan pada pemilihan sekelompok sensor yang dapat bekerja secara optimal berdasarkan pada perhitungan korelasi jarak. Berdasarkan pada percobaan pada data experimental dari sebuah struktur jembatan, algoritma yang digagas dapat mengurangi jumlah akselerometer yang diperlukan hingga 80% untuk menyusun model prediktif tanpa mengurangi atau bahkan meningkatkan akurasi dari model prediktif akselerometer sebesar 1 hingga 1,3%. Lebih lanjut, algoritma yang digagas dapat mendekteksi keberadaan kerusakan struktur dengan baik, serta mampu mengkarakterisasi tingkat kerusakan dari struktur berdasarkan pada perubahan standar deviasi dari residu kuadrat.
Penentuan Jalur Diagnostik Penyakit Berbasis Konsep Pembelajaran Mesin: Studi kasus Penyakit Hepatitis C Jimmy Tjen; Valentino Pratama
Journal of Applied Computer Science and Technology Vol 4 No 2 (2023): Desember 2023
Publisher : Indonesian Society of Applied Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52158/jacost.v4i2.556

Abstract

Hepatitis is considered to be one of the most dangerous diseases, which often leads to death if not handled properly. Thus, early detection via precise diagnosis is needed in order to prevent the unfortunate event. This research aims to provide a novel hepatitis C diagnosis based on the machine learning algorithm, which is the classification tree from the decision tree learning and the distance correlation, which measures the Euclidean distance between 2 vectors. In particular, the goal is to develop a low computational cost yet precise algorithm for diagnosing the possibility of whether a person is being infected with Hepatitis C or not. Based on the experiment, the distance correlation-based classification tree algorithm outperforms the classical classification tree algorithm by around 3% while using only 7 features instead of 12 as in the classical algorithm. Furthermore, the algorithm identified albumin (ALB), Creatinine (CREA), Bilirubin (BIL), Aspartate Transaminase (AST) and Cholesterol (CHOL) as significant risk factors in determining whether someone is potentially infected with hepatitis C or not, with Creatinine is identified as the most important parameter among all 5 parameters mentioned above.
Perancangan Sistem Pakar Penyakit Demam Berdarah Menggunakan Metode Gradient Boosting Decision Tree Valentina, Cecilia; Tjen, Jimmy
MDP Student Conference Vol 4 No 1 (2025): The 4th MDP Student Conference 2025
Publisher : Universitas Multi Data Palembang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35957/mdp-sc.v4i1.11200

Abstract

Dengue fever is a disease transmitted through mosquito bites and can cause high death rates in several countries . This disease is most commonly found in countries with a tropical climate. Therefore, technology utilization has been implemented to help people to predict dengue fever. This research design an expert system using the Gradient Boosting Decision Tree (GBDT) method to classification a symptoms of dengue fever. This research used a dataset from Kaggle website and this data was analyzed and resulted in accuracy of 89%, a recall of 88,79%, and a precision of 69,96%. So, it was able to provide an accurate prediction of dengue fever through the GBDT method. The classification result was then adapted into mobile based application with a UI/UX design so that it can directly interact with users.
Perancangan UIUX Aplikasi Pemantau Kelembaban Tanah dan Suhu Udara Pada Tanaman Buah Naga Sabila, Qolbi Sin; Tjen, Jimmy
MDP Student Conference Vol 4 No 1 (2025): The 4th MDP Student Conference 2025
Publisher : Universitas Multi Data Palembang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35957/mdp-sc.v4i1.11201

Abstract

The increase in the number of dragon fruit commodities is greatly influenced by the growth and quality of the plant itself. Soil moisture and air temperature are the main factors that must be considered by farmers in the cultivation of dragon fruit plants. There is a need for regular monitoring of dragon fruit plants to maximize the growth of these plants. This research aims to design a User Interface (UI) display for the application of monitoring soil moisture and air temperature in dragon fruit plants. This application is based on Android to be more flexible when used. This monitoring application is supported by the use of IoT technology sensor devices connected to a microcontroller, this application can present real-time data about plant conditions, such as soil moisture levels and air temperature in a visual form that is easy to understand for users. The result of this research is a UI design with ease of providing information to users so that it can enable users to improve decision-making accuracy in monitoring soil moisture and air temperature in dragon fruit plants. This is expected to have a positive impact on the productivity of users in caring for dragon fruit plants and can maintain the quality of the harvest.
Beauty Product Recommendation from Customer Reviews Based on Multinomial Naïve Bayes Algorithm Therence, Claretty; Tjen, Jimmy
Informatics and Digital Expert (INDEX) Vol. 7 No. 1 (2025): INDEX, Mei 2025
Publisher : LPPM Universitas Perjuangan Tasikmalaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36423/index.v7i1.2208

Abstract

The cosmetic industry’s increasing dependence on online platforms makes understanding customer sentiment essential for brand success. The study addresses the challenge of understanding customer sentiment regarding concealer brands on the Shopee platform, a critical aspect for brand managers and consumers alike. The problem stems from the vast amount of reviews that can be overwhelming for stakeholders looking to extract actionable insights. To solve this, we applied a Naïve Bayes classification approach with a Bag of Words (BoW) model to analyze a dataset containing 3,920 customer reviews. This dataset was divided into 1,120 training samples and 280 testing samples of each of 10 brands, following an 80:20 ratio. The analysis yields recurring positive and negative sentiment themes, achieving test accuracy of 87.95% and a training accuracy of 94.31%. Findings reveal key consumer preferences and lead to specific product recommendations, such as Mad For Makeup and Luxcrime for high coverage, Guele for lightweight formulas. Additionally, tailored marketing strategies like enhancing packaging and engaging with consumers through social media are suggested. This research provides actionable insights for brand managers, contributing to sentiment analysis literature in the cosmetics sector.
Pengaruh Jenis Stopwords terhadap Akurasi Model Multinomial Naïve Bayes dalam Proses Sentimen Analisis Tjen, Jimmy
Jurnal Buana Informatika Vol. 16 No. 01 (2025): Jurnal Buana Informatika, Volume 16, Nomor 01, April 2025
Publisher : Universitas Atma Jaya Yogyakarta

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

Abstract

Implementing machine learning in business has enabled producers and sellers to assess product quality by analyzing customer reviews through Sentiment Analysis (SA). This study investigates the impact of different stopword categories on the accuracy of the Multinomial Naïve Bayes (MNB) model for SA. This research considered ten stopword categories: general, conjunctions, slang, temporal terms, nouns, pronouns, interjections, adverbs, and single-letter words. A Friedman test conducted on commentary from three shoe products revealed that removing conjunction stopwords (MNB-conjunction) could potentially improve the predictive accuracy of the MNB model for SA by approximately 1%. A T-test further validated this result, showing that two out of three datasets provided evidence that MNB-conjunction outperformed the MNB model without removing stopwords.
Prediksi Safety Stock Penjualan Produk Pakaian Berbasis Model DR-ARIMA (Studi Kasus: Veruby Store) Verita, Nengsy; Tjen, Jimmy
Jutisi : Jurnal Ilmiah Teknik Informatika dan Sistem Informasi Vol 14, No 2: Agustus 2025
Publisher : STMIK Banjarbaru

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35889/jutisi.v14i2.2854

Abstract

Effective inventory management has become a crucial aspect in the fashion retail industry, especially in responding to fluctuating demand. This study aimed to predict safety stock needs using the DR-ARIMA (3,0,6) model, an enhancement of ARIMA that considers demand response and error analysis. Predictions were validated with Root Mean Square Error and used to establish lower and upper sales projection limits for safety stock calculation. The model demonstrated good accuracy for product categories with stable sales patterns such as tops, while showing limitations in categories with more volatile demand like bottoms. These findings highlight the imporantance of integrating trend analysis and customer behavior data to develop more adaptive and responsive stock management strategies amid market dynamics. Key Words: Safety Stock; ARIMA; Demand Response; Fashion Retail AbstrakPengelolaan persediaan yang efektif menjadi aspek penting dalam industri riteal fashion, terutama dalam menghadapi permintaan yang fluktuatif, penelitian ini bertujuan memprediksi kebutuhan safety stock menggunakan model DR-ARIMA (3,0,6), yang merupakan pengembanagn dari ARIMA yang memperhitungkan respon permintaan dan analisis kesalahan. Hasil prediksi divalidasi dengan Root Mean Square Error dan digunakan untuk menentukan batas bawah dan atas proyeksi penjualan sebagai dasar penentuan safety stock. Model menunjukkan tingkat akurasi yang baik untuk kategori produk dengan pola penjualan stabil seperti atasan, namun memiliki keterbatasan dalam kategori dengan pola permintaan fluktuatif seperti bawahan. Temuan ini menegaskan pentingnya integrase analisis tren dan data perilaku pelanggan demi pengelolaan stok yang lebih adaptif dan responsif terhadap dinamika pasar. 
Identifikasi Parameter Kualitas Bahan Pangan dengan Metode Entropy-Based Subset Selection (E-SS) (Studi Kasus: Minuman Anggur) Tjen, jimmy
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 11 No 1: Februari 2024
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.20241116850

Abstract

Penelitian ini bertujuan untuk membangun sebuah algoritma yang dapat mengidentifikasi parameter dari bahan pangan yang mempengaruhi kualitas dari bahan makanan tersebut menggunakan algoritma pemilihan himpunan bagian berbasis entropi dan metode pohon klasifikasi dari pembelajaran pohon keputusan. Metode pemilihan himpunan bagian berbasis entropi secara khusus merupakan sebuah algoritma yang bertujuan untuk memilih sekumpulan dari parameter yang memiliki hubungan entropi yang baik satu sama lain, sehingga dapat menghasilkan model prediktif yang optimal. Untuk memvalidasi performa dari algoritma yang digagas, penelitian ini mengambil sampel dari minuman anggur merah dan putih yang berasal dari negara Portugal. Berdasarkan pada percobaan yang telah dilakukan, diperoleh hasil bahwa algoritma yang digagas dapat memprediksi kualitas dari anggur putih dengan akurasi hingga 97,8 % dan 96,25% untuk kualitas anggur merah. Dimana, nilai ini lebih tinggi dari metode pohon klasifikasi klasik, dan algortima yang digagas hanya membutuhkan jumlah parameter yang lebih sedikit ( hanya 2 hingga 5 dari total 11 parameter input yang ada) jika dibandingkan dengan metode klasik. Lebih lanjut, berdasarkan pada percobaan yang telah dilakukan, diperoleh temuan bahwa parameter yang paling menentukan kualitas dari anggur putih adalah tingkat keasaman, kadar alkohol, pH dan kandungan klorit. Sedangkan untuk anggur merah, kualitas secara dominan ditentukan oleh kandungan sisa gula, densitas minuman dan kandungan dari sulfur oksida.
Pendeteksian Lahan dari Citra High Resolution Remote Sensing Berbasis Metode Single-class Convolutional Neural Network Tjen, Jimmy
JEPIN (Jurnal Edukasi dan Penelitian Informatika) Vol 11, No 1 (2025): Volume 11 No 1
Publisher : Program Studi Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26418/jp.v11i1.89587

Abstract

pendektesian lahan dengan memanfaatkan citra satelit telah berkembang pesat sebagai suatu solusi dalam mengidentifikasi jenis lahan. Namun, hal ini terkendala oleh keterbatasan algoritma seperti convolutional neural network atau CNN dalam mengidentifikasi permukaan lahan dengan citra yang mirip. Penelitian ini mengagas konsep CNN baru dengan mengabungkan sekelompok model kecil yang hanya mempelajari 1 kelas, yang disebut sebagai single-class CNN atau SC-CNN. Hasil validasi pada citra satelit Gaofen-2 menunjukan bahwa metode SC-CNN mampu meningkatkan akurasi model prediktif dari metode CNN sebesar 3% dalam mengidentifikasi permukaan lahan. Lebih lanjut, meskipun dengan menggunakan data yang lebih sedikit (hanya 480 citra), metode SC-CNN menunjukan kualitas prediktif yang sama dengan metode CNN yang menggunakan 800 citra. Hasil ini menunjukan potensi dari SC-CNN dalam mengklasifikasikan citra satelit, sekaligus sebagai metode klasifikasi gambar dengan jumlah citra yang terbatas.
Co-Authors Alvin Lesmana Danuwinata, Edward Revaldo Eva Gultom Felix Liando Genrawan Hoendarto Ilucky Salim Indri Sari Dewi Jasmine Putri Halim Paskalia Kartini Riyadi Jimmy Iskandar Sabila, Qolbi Sin Saputra, Andre Tendean, Sandi Therence, Claretty Tony Darmanto Valentina, Cecilia Valentino Pratama Vedano Gustine Venicia Lauren Verita, Nengsy Willay, Thommy Willim, Andre Prasetya