cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Eferoni ndruru
Contact Email
jitsiinfo@gmail.com
Phone
+6285297531507
Journal Mail Official
jitsiinfo@gmail.com
Editorial Address
Komplek perumahan new pratama asri, blok c.47
Location
Kab. deli serdang,
Sumatera utara
INDONESIA
JITSI : Jurnal Informatika Dan Teknologi Sistem Informasi
Published by PT. Ndruru Jaya Mandiri
ISSN : -     EISSN : 31236650     DOI : DOI: https://doi.org/10.67763/jitsi.v1i2.71
Core Subject :
JITSI: Jurnal Informatika dan Teknologi Sistem Informasi merupakan jurnal ilmiah yang diterbitkan sebagai media publikasi hasil penelitian, kajian ilmiah, dan pengembangan ilmu pengetahuan di bidang informatika, teknologi informasi, dan sistem informasi. Jurnal ini bertujuan menjadi wadah bagi akademisi, peneliti, praktisi, mahasiswa, serta profesional untuk menyebarluaskan hasil penelitian yang berkualitas, inovatif, dan memberikan kontribusi terhadap perkembangan ilmu pengetahuan maupun penerapannya dalam masyarakat. Ruang lingkup publikasi JITSI meliputi berbagai bidang, antara lain Rekayasa Perangkat Lunak, Sistem Informasi, Kecerdasan Buatan (Artificial Intelligence), Machine Learning, Data Mining, Big Data, Data Science, Internet of Things (IoT), Keamanan Siber, Komputasi Awan (Cloud Computing), Jaringan Komputer, Multimedia, Mobile Computing, Sistem Pendukung Keputusan, Geographic Information System (GIS), Blockchain, Human-Computer Interaction (HCI), Teknologi Web, Basis Data, serta topik lain yang relevan dengan perkembangan informatika dan teknologi sistem informasi.
Arjuna Subject : -
Articles 10 Documents
 1 
Analisa Perbandingan Proses Algoritma Elias Gamma Code dengan algoritma Elias Delta Code Dalam Kompresi File Audio Video Interleave Pengalaman ndruru
Interaksi : Jurnal Informatika Dan Teknologi Sistem Informasi Vol 1 No 1 (2025): November 2025
Publisher : PT. Ndruru Jaya Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.67763/jitsi.v1i1.60

Abstract

Abstract− Data compression is a method in computer science for reducing data size or compressing data. The data to be compressed is smaller in size than the previous data with the aim of saving storage. If data compression is carried out, the storage space required is small. Apart from being more efficient, compression can also result in faster data measurement times. As technology develops nowadays, data has a very important role, the large amount of data stored on memory or hard disk will result in a large increase in the used capacity of the storage media. There are two techniques that can be used to compress data, namely lossy compression and lossless compression. Lossy Compression is data file compression where the decompression results of the compressed data are not the same as the original data because there is information lost, but it can still be tolerated by eye perception. Lossy data compression will be effective when applied to images, films and digital sound. Meanwhile, Lossless compression is data compression where the decompression results of the compressed data are the same as the original data and no information is lost. One of the compression algorithms that can be used is the Elias Delta Code Algorithm which is a compression algorithm created by Peter Elias. The Elias Gamma Code code adds the length of the code in unary (α). The next code, δ(delta), is then added to the binary code (β). Elias Delta Code for positive integers, a bit more complex to construct. This algorithm can also compress large files to reduce the size of the file.
Klasifikasi data dengan kategori Siswa dan siswi BARU dengan algoritma K-MEANS CLUSTERING Leni Mawarni Halawa
Interaksi : Jurnal Informatika Dan Teknologi Sistem Informasi Vol 1 No 1 (2025): November 2025
Publisher : PT. Ndruru Jaya Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.67763/jitsi.v1i1.61

Abstract

Abstract− Students are an input component in the education system that has a big influence on school identity, students are a prioritized component in the education process, so that they become quality human beings in accordance with national education goals (Budi Sutedjo et al. 2010). In the teaching and learning process there are categories of students, especially new students. This category is given to students based on the activities or values obtained during the teaching and learning process.
PEMANFAATAN METODE FILTERING GAUSSIAN DALAM MEPERBAIKI NOISE PADA GAMBAR Desti Sonya Buulolo
Interaksi : Jurnal Informatika Dan Teknologi Sistem Informasi Vol 1 No 1 (2025): November 2025
Publisher : PT. Ndruru Jaya Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.67763/jitsi.v1i1.62

Abstract

Noise adalah efek samping dari penggunaan sensor elektronik yang dipakai untuk mengumpulkan cahaya. Ibaratnya kalau anda memanen padi, noise adalah kulit padi sementara beras adalah fotonya. Dia adalah sesuatu yang tidak diinginkan, namun akan selalu muncul sebagai akibat dari ketidak sempurnaan kinerja sensor. Noise pada foto ditengarai sebagai penyebab berkurangnya detail dan tampak tidak enak dilihat. Gangguan pada citra umumnya berupa variasi intensitas suatu pixel yang tidak berkolerasi dengan pixel tetangganya, pixel yang mengalami gangguan umumnya memiliki frekuensi tinggi, setiap gangguan pada citra dinamakan noise. Citra yang mengandung noise memerlukan langkah-langkah perbaikan untuk meningkatkan kualitas citra. Tujuan utama dari peningkatan kualitas citra adalah untuk memproses citra sehingga citra yang dihasilkan lebih baik dari pada citra aslinya. Metode filter gaussian bertujuan untuk mengurangi noise dengan cara menentukan kernel matriks dan bekerja dengan menggantikan nilai intensitas setiap pixel citra masukan dengan rata-rata dari nilai pembobotan kernel untuk setiap pixel-pixel tetangganya dan pixel itu sendiri. Untuk memperbaiki citra akibat noise dapat diatasi dengan proses filtering, filtering yang terdiri dari filtering gaussian. Filtering gaussian adalah suatu filter dengan nilai pembobotan pada setiap pixel dipilih berdasarkan bentuk fungsi gaussian. Filter ini sangat baik dan sering digunakan untuk menghilangkan noise yang bersifat sebaran normal.
Kombinasi Metode (RLE) dengan Algoritma Arithmetic Coding dalam kompresi file citra Ferdian Gowasa
Interaksi : Jurnal Informatika Dan Teknologi Sistem Informasi Vol 1 No 1 (2025): November 2025
Publisher : PT. Ndruru Jaya Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.67763/jitsi.v1i1.63

Abstract

Abstract−When we send digital images using communication lines or the internet. With a large file size, it causes problems with sending images, namely long delivery times. Another problem is that all images require quite large storage media. This can cause quite serious problems when images are stored in a database with limited storage media. So we are trying to find a solution that can reduce the size of image files. One of the solutions to overcome the problem above is to perform compression. Image compression is the general process of minimizing the number of bits that represent an image so that the file size becomes smaller. There are two types of compression, namely lossless type and lossy type compression. Lossless type compression is compression where the quality of the compression results does not decrease when reconstructed. Meanwhile, the lossy type will result in the resulting image quality being much lower than the quality of the original image when reconstructed
Diagnosa Dampak Penggunaan Softlens Menggunakan Certainty Factor BerbasisAndroid Vivi Yohana Zebua
Interaksi : Jurnal Informatika Dan Teknologi Sistem Informasi Vol 1 No 1 (2025): November 2025
Publisher : PT. Ndruru Jaya Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.67763/jitsi.v1i1.64

Abstract

An expert system (Expert System) was first developed by the Artificial Intelligence (AI) community in the mid-1960s. The expert system that first appeared was the General-purpose Problem Solver (GPS) developed by Newel and Simon (Turban, l995). An expert system is a computer program that simulates the judgment and behavior of humans or organizations that have expert knowledge and experience in a particular field. Usually systems like this contain a knowledge base containing accumulated experience and a set of rules for applying basic knowledge to each specific situation. In the world of technology, especially technology that operates in the field of communication, such as cellular communication technology, has created major changes. The rapid development of this technological device has given rise to a variety of new functions apart from its main function as a communication tool. Today's mobile phones are supported by the Android operating system, where Android is a Linux-based mobile phone operating system. Android is the latest breakthrough platform for mobile devices which is very popular with mobile device users, this is because the system performance on Android is easy to operate with an attractive appearance and can be
Penerapan Metode Linear Regression Untuk Memprediksi Harga Rumah Muhammad Arizal Dwisakti; Muhammad Ridho Pramana; Riski Juliandri; Taronisokhi Zebua
Interaksi : Jurnal Informatika Dan Teknologi Sistem Informasi Vol 1 No 2 (2026): Mei
Publisher : PT. Ndruru Jaya Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.67763/jitsi.v1i2.66

Abstract

House prices are an important indicator in the property sector and are influenced by various factors, including physical characteristics of the building and location. This study aims to analyze and predict house prices using a linear regression method by utilizing several variables, namely land area, building area, number of bedrooms, number of bathrooms, parking availability, and distance to the city center. The data used in this study are secondary data collected through a web scraping process and are focused on houses with a price range of 300700 million rupiah to represent the middle-market segment. The research stages include data preprocessing, Pearson correlation analysis, multicollinearity testing, multiple linear regression modeling, and model performance evaluation using the coefficient of determination (R²) and Root Mean Square Error (RMSE). The dataset is divided into 80% training data and 20% testing data. The results show that the constructed linear regression model achieves an R² value of 0.3078, indicating that the independent variables are able to explain 30.78% of the variation in house prices. The RMSE value of 117,482,242 indicates that prediction errors remain relatively high due to the wide variation in house prices. The correlation analysis results reveal that the number of bathrooms and the distance to the city center have a relatively stronger relationship with house prices compared to other variables. This study demonstrates that linear regression can be used as an initial approach for house price prediction; however, it still has limitations in explaining overall price variations. Therefore, future research is expected to improve prediction performance by incorporating additional variables or applying more advanced modeling methods.
Penerapan Metode Naïve Bayes Untuk Mengklasifikasi Bunga Iris Desman Karya Jaya Zega; Desman Karya Jaya Zega; Muhammad rizki arisandi berutu; David Soteriel Agung Ndruru; Putri Shabna Dewi Sinaga; Erlangga Oktaviano; Anisa salsabila
Interaksi : Jurnal Informatika Dan Teknologi Sistem Informasi Vol 1 No 2 (2026): Mei
Publisher : PT. Ndruru Jaya Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.67763/jitsi.v1i2.67

Abstract

Penelitian ini memaparkan evaluasi komparatif mengenai algoritma Naive Bayes dan Decision Tree dalam konteks penerapan pada beragam domain data. Domain-domain yang dieksplorasi mencakup pengenalan spesies Iris, penentuan komposisi material daging, kategorisasi subjek, serta penilaian tingkat kemiringan di sektor pariwisata yang membutuhkan analisis yang cermat. Tujuan utama dari studi ini adalah untuk menilai efektivitas, keunggulan khas, serta batasan yang dimiliki oleh kedua model prediktif tersebut ketika dihadapkan pada karakteristik data yang berbeda. Metodologi penelitian melibatkan implementasi sistematis dari kedua algoritma pada serangkaian dataset spesifik dengan prosedur eksperimen yang terstruktur. Pengukuran kinerja didasarkan pada metrik-metrik standar, yaitu akurasi, presisi, recall, dan F1-score. Hasil analisis menunjukkan bahwa kedua algoritma menampilkan tingkat kinerja yang sebanding, namun dengan titik kekuatan yang berbeda-beda yang bergantung erat pada sifat data yang dianalisis. Decision Tree terbukti menawarkan interpretabilitas yang superior dan memiliki kapabilitas lebih baik dalam memodelkan hubungan data non-linear. Sebaliknya, Naive Bayes menunjukkan efisiensi yang optimal, terutama pada kasus di mana fitur-fitur memiliki tingkat independensi tinggi dan ketika diolah menggunakan jumlah data pelatihan yang besar. Temuan-temuan ini memberikan kontribusi penting dalam mengidentifikasi skenario aplikasi yang paling sesuai untuk masing-masing algoritma dalam menyelesaikan berbagai permasalahan data mining secara efektif. Hasil pengujian menunjukkan bahwa metode Naive Bayes mampu mencapai akurasi sebesar 93,3%, dengan nilai precision, recall, dan F1-score yang tinggi pada setiap kelas. Hasil ini menunjukkan bahwa metode Naive Bayes memiliki kinerja yang baik dan efektif dalam melakukan klasifikasi bunga Iris..
Sistem prediksi cuaca sederhana menggunakan fuzzy logic Muhammad Raka Abdurrahman; Muhammad Raka Abdurrahman; Niel Berkat Harefa; Mia Sefitri Zalukhu; Berliana Yohana Pasaribu
Interaksi : Jurnal Informatika Dan Teknologi Sistem Informasi Vol 1 No 2 (2026): Mei
Publisher : PT. Ndruru Jaya Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.67763/jitsi.v1i2.68

Abstract

Climate change and increasing daily weather uncertainty, particularly extreme rainfall events, make fast, simple, and easy-to-understand weather forecast information increasingly important for decision-making at the local level. This condition encourages the need to develop a weather prediction system that is not only accurate, but also computationally lightweight and interpretable. This study aims to design and implement a simple daily weather prediction system using the Mamdani Fuzzy Inference System (FIS) method. This study uses an experimental quantitative approach by utilizing historical weather data for Medan City for the period 2023–2025 which includes variables of average temperature, rainfall, wind speed, and air pressure. Data were collected through documentation techniques and analyzed using fuzzification, IF–THEN rule-based inference, and defuzzification with the Centroid of Area method. The results show that the developed fuzzy system is able to classify daily weather conditions into Sunny, Cloudy, and Rainy categories consistently and easily interpreted. This system provides a practical contribution as a simple and adaptive local-scale weather prediction tool, as well as a scientific contribution in clarifying the application of the minimalist Mamdani FIS for weather prediction. The study's conclusion confirms that the fuzzy logic approach is effective as a simple weather prediction system, and further research is recommended to increase the number of variables, expand the study area, and compare the fuzzy method with other artificial intelligence approaches.
Sistem Pakar Diagnosa Penyakit Hewan Ternak Menggunakan Metode Rule Based Expert System Mutiara NR Ge'e; Mutiara NR Ge’e; Roito Siburian; Rut F Manalu3; Melissa P Hutabarat; Adira Arifandi; M. Rizky
Interaksi : Jurnal Informatika Dan Teknologi Sistem Informasi Vol 1 No 2 (2026): Mei
Publisher : PT. Ndruru Jaya Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.67763/jitsi.v1i2.69

Abstract

Livestock health is a crucial factor in maintaining the productivity and sustainability of the livestock sector. The limited availability of veterinary medical personnel, particularly in rural areas, prevents the rapid and accurate diagnosis of livestock diseases. This situation encourages the use of technology as a tool for early disease identification. Cattle, as a livestock commodity with high economic value in Indonesia, are reportedly susceptible to various diseases, such as three-day fever, diarrhea, mastitis, and foot-and-mouth disease (FMD), which can reduce productivity. This article was compiled using a literature review method, reviewing several previous studies that discussed the application of rule-based expert systems in cattle disease diagnosis. The study focuses on the concept of expert systems, forward chaining reasoning methods, and their potential application as decision support systems in the field of livestock health. The results are expected to provide an overview and serve as an initial reference for the development of future livestock health support systems.
Klasifikasi Sentimen Ulasan Produk E-Commerce Menggunakan Naive Bayes Anwar Musyaddad Rangkuti; Hasbi Mustafa; Adi Guna Marios Tarihoran; Andre Andre
Interaksi : Jurnal Informatika Dan Teknologi Sistem Informasi Vol 1 No 2 (2026): Mei
Publisher : PT. Ndruru Jaya Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.67763/jitsi.v1i2.71

Abstract

Perkembangan e-commerce di Indonesia telah menghasilkan volume ulasan produk yang sangat besar, yang dapat dimanfaatkan untuk memahami persepsi dan kepuasan pelanggan. Namun, analisis manual terhadap ribuan komentar tersebut tidak efisien dan berpotensi bias. Penelitian ini bertujuan untuk mengklasifikasikan sentimen pada ulasan produk e-commerce berbahasa Indonesia menggunakan algoritma Naïve Bayes Classifier (NBC) dengan metode pembobotan Term Frequency–Inverse Document Frequency (TF–IDF). Permasalahan utama yang dihadapi adalah bentuk data ulasan yang tidak terstruktur, bervariasi secara linguistik, dan sering kali ambigu dalam konteks makna. Untuk mengatasinya, penelitian ini menerapkan tahapan preprocessing teks seperti cleaning, tokenizing, stopword removal, dan stemming untuk menghasilkan data bersih sebelum dilakukan proses klasifikasi. Dataset yang digunakan merupakan kumpulan ulasan konsumen dari platform e-commerce yang telah diberi label sentimen positif dan negatif secara manual. Data kemudian dibagi menjadi tiga skenario pelatihan dan pengujian dengan rasio 70:30, 80:20, dan 90:10. Hasil pengujian menunjukkan bahwa model Naïve Bayes mampu mencapai akurasi tertinggi sebesar 94,09%, dengan nilai presisi 95,17%, recall 98,52%, dan F1-score 96,81% pada rasio 90:10. Hasil tersebut membuktikan bahwa kombinasi TF–IDF dan Naïve Bayes Classifier efektif dalam mengklasifikasikan opini publik terhadap produk e-commerce secara otomatis dan efisien. Temuan ini diharapkan dapat membantu pelaku bisnis dalam memahami persepsi pelanggan serta mendukung strategi pemasaran berbasis data yang lebih tepat sasaran. Kata Kunci: Klasifikasi Sentimen, Ulasan Produk, E-Commerce, Naïve Bayes

Page 1 of 1 | Total Record : 10

 1 

Filter by Year

2025 2026


Reset
Filter By Issues
All Issue Vol 1 No 2 (2026): Mei Vol 1 No 1 (2025): November 2025