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Jurnal Teknologi Informasi : Jurnal Keilmuan dan Aplikasi Bidang Teknik Informatika
Published by Universitas Palangka Raya
ISSN : 1907896X     EISSN : 26560321     DOI : https://doi.org/10.47111/JTI
Core Subject : Science, Engineering,
Computer Science & IT Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering Library & Information Science
Jurnal Teknologi Informasi (JTI) diterbitkan adalah Jurnal Jurusan Teknik Informatika Universitas Palangka Raya dengan ISSN 1907-896X, E-ISSN 2656-0321. Jurnal Teknologi Informasi (JTI) merupakan Jurnal Keilmuan dan Aplikasi Bidang Teknik Informatika yang menyajikan hasil penelitian yang fokus pada bidang informatika. Jurnal Teknologi Informasi (JTI) terbit dua kali dalam satu tahun (Januari dan Agustus). JTI ini fokus mempublikasi hasil penelitian orisinal yang belum diterbitkan di mana pun, isu yang dipublikasi oleh JTI meliputi pengembangan ilmu pengetahuan komputer dan informatika, fokus pada sains ilmu komputer, teknologi komputer tepat guna, dan rancang bangun sistem informasi.
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 465 Documents
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PERBANDINGAN MODEL PREDIKSI DATA MINING DALAM MEMPREDIKSI KONSENTRASI POLUTAN KARBON MONOKSIDA (CO) DI JAKARTA Amanu, Rendy Syahril; Ramadhan, Faiz Ahza; Saputra , Agung Hari
Jurnal Teknologi Informasi: Jurnal Keilmuan dan Aplikasi Bidang Teknik Informatika Vol. 18 No. 1 (2024): Jurnal Teknologi Informasi : Jurnal Keilmuan dan Aplikasi Bidang Teknik Inform
Publisher : Universitas Palangka Raya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47111/jti.v18i1.12451

Abstract

DKI Jakarta, as the capital of Indonesia, faces serious challenges in terms of air quality. Carbon monoxide (CO) is one of the main air pollutants in Jakarta that is harmful to human health and the environment. Data mining is a method that can be used to predict situations based on a model. The study aims to compare data mining models with the best-performing methods to predict carbon monoxide pollutants in Jakarta. The predictive data mining model of the python library is tested and evaluated based on the evaluation metrics of MASE, RMSSE, MAE, RMSE, MAPE and SMAPE values. The model test results showed that K Neighbors with the Conditional Deseasonalize & Detrending model had the best metric evaluation value to predict CO concentration with the value evaluation metrics of MASE 0.2942, RMSSE 0.2483, MAE 2.7362, RMSE 3.3863, MAPE 0.1975 and SMAPE 0.01993. Overall, K Neighbors with the Conditional Deseasonalize & Detrending model shows good performance to predict CO concentrations in Jakarta, but further adjustments are needed to improve accuracy.
IMPLEMENTASI CONTENT-BASED FILTERING MENGGUNAKAN TF-IDF AND COSINE SIMILARITY UNTUK SISTEM REKOMENDASI RESEP MASAKAN Priskila, Ressa; Nova Noor Kamala Sari; Putu Bagus Adidyana Anugrah Putra
Jurnal Teknologi Informasi: Jurnal Keilmuan dan Aplikasi Bidang Teknik Informatika Vol. 18 No. 1 (2024): Jurnal Teknologi Informasi : Jurnal Keilmuan dan Aplikasi Bidang Teknik Inform
Publisher : Universitas Palangka Raya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47111/jti.v18i1.12543

Abstract

Many housewives are still confused about what dishes they will cook with existing food ingredients. Most housewives get recipe ideas from the website. Recipes from the website have the advantage of being easily accessible, but the disadvantages are sometimes troublesome for users because they have to choose a recipe from which site because there are many sites that contain the same recipe, and most of the recipe websites on the internet do not have a feature to search recipes based on the ingredients they have. The aim of this research is to implement a content-based filtering method using TF-IDF and cosine similarity for a recipe recommendation system. The TF-IDF and cosine similarity models are used to find similarity values between material data in the database and the query entered by the user in the search form. The sample data used in this research is 30 recipe data points taken from the website makapahariini.com. As a result, this system displays recipe recommendations that match the query of ingredients inputted by the user on the search form, and based on the test results using root mean square error (RMSE), it can be said that the recommendation system with the content-based filtering method that has been implemented produces quite good recommendations with a value of 0.356359182.
PENGGUNAAN ALGORITMA HEBB DALAM POLA PENGENALAN HURUF Kristianti, Novera; Widiatry, Widiatry
Jurnal Teknologi Informasi: Jurnal Keilmuan dan Aplikasi Bidang Teknik Informatika Vol. 18 No. 1 (2024): Jurnal Teknologi Informasi : Jurnal Keilmuan dan Aplikasi Bidang Teknik Inform
Publisher : Universitas Palangka Raya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47111/jti.v18i1.12561

Abstract

Dalam ilmu komputer, Jaringan Syaraf Tiruan (JST) adalah pendekatan populer yang bertujuan untuk menyelesaikan berbagai permasalahan, seperti pengenalan pola atau klasifikasi, melalui pembelajaran. Penelitian ini mengeksplorasi penggunaan algoritma Hebb Rule dalam konteks pengenalan pola huruf menggunakan jaringan syaraf tiruan. Jaringan syaraf tiruan adalah model pemrosesan informasi yang terinspirasi oleh struktur jaringan syaraf biologis manusia. Algoritma Hebb Rule digunakan untuk melatih jaringan agar dapat mengidentifikasi dan membentuk asosiasi antara pola input dan output. Penelitian ini fokus pada penggunaan algoritma Hebb Rule dalam mengenali pola huruf “T” dan “U” dalam format matriks 5x5 dengan representasi data bipolar, di mana “X” diwakili sebagai -1 dan “O” diwakili sebagai 1. Metodologi penelitian mencakup identifikasi masalah, tujuan penelitian, pengenalan pola huruf, penerapan algoritma Hebb Rule, dan hasil pola. Hasil penelitian menunjukkan bahwa pola pada huruf “T” dan “U” dapat diidentifikasi menggunakan algoritma Hebb dengan nilai bersih 32 dan -32, masing-masing. Penelitian ini juga mencakup perubahan bobot dan bias pada jaringan Hebb melalui serangkaian iterasi, serta perhitungan nilai aktivasi jaringan untuk menentukan keberhasilan pengenalan pola. Kesimpulannya, penelitian ini memberikan wawasan yang lebih dalam tentang penggunaan algoritma Hebb dalam pengenalan pola huruf dan potensinya dalam pengembangan aplikasi praktis.
PERBANDINGAN ALGORITMA MACHINE LEARNING UNTUK ANALISIS SENTIMEN PADA ULASAN HOTEL Pranatawijaya, Viktor Handrianus; Efrans Christian
Jurnal Teknologi Informasi: Jurnal Keilmuan dan Aplikasi Bidang Teknik Informatika Vol. 18 No. 1 (2024): Jurnal Teknologi Informasi : Jurnal Keilmuan dan Aplikasi Bidang Teknik Inform
Publisher : Universitas Palangka Raya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47111/jti.v18i1.12581

Abstract

The paper extensively explores machine learning algorithms for evaluating sentiments in hotel reviews, particularly within the tourism and hospitality industry. It underscores the importance of precise reviews in utilizing artificial intelligence for improved operational efficiency, revenue optimization, and heightened customer satisfaction. Notably, supervised machine learning algorithms like Gradient Boosting, Support Vector Machine, and K-Nearest Neighbor are highlighted for offering recommendations based on reviews to predict user preferences. The research methodology involves data scraping, cleaning, preprocessing, and labeling, followed by training and testing the chosen machine learning algorithms. Results indicate that the Support Vector Machine algorithm demonstrated superior performance with accuracy 0.8553, precision 0.8433, recall 0.8553, dan F1-score 0.8424, suggesting its appropriateness for sentiment analysis in hotel reviews. The paper concludes by recommending the implementation of the Support Vector Machine model for sentiment analysis in hotel reviews in Palangka Raya, Indonesia, and proposes avenues for further industry development and enhancement.
RANCANG BANGUN APLIKASI ABSENSI GURU DAN STAF TU DENGAN PENERAPAN GEOLOCATION DAN FINGERPRINT BERBASIS ANDROID DI SMK GKE MANDOMAI Berkati, Axel; Licantik; Nugrahaningsih, Nahumi; Lestari, Ariesta; Sylviana, Felicia
Jurnal Teknologi Informasi: Jurnal Keilmuan dan Aplikasi Bidang Teknik Informatika Vol. 18 No. 1 (2024): Jurnal Teknologi Informasi : Jurnal Keilmuan dan Aplikasi Bidang Teknik Inform
Publisher : Universitas Palangka Raya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47111/jti.v18i1.12590

Abstract

Attendance is one of the work assessments and as proof of attendance is very important. At this time, the attendance system used by SMK GKE Mandomai is still implementing a manual attendance system, which is every day to sign at every date of entry to work, and attendance is done 2 times, namely entry attendance and return attendance. This manual attendance system has the potential to cause some problems, such as attendance papers that are easily lost or scattered and the possibility of manipulating attendance data. As a solution, namely by designing an attendance application with the application of geolocation and fingerprint on android as security when doing absences, in order to prevent teachers and TU staff who are outside the school location to fill absences. The development method used in designing this system is the Waterfall model with its stages, namely Software Requirements Analysis, Design, Coding, and testing. System design using UML model, implement coding using JavaScript, React Native framework and React JS, backend using Firebase. the results of this system design, produce Android-based attendance system for teachers and staff TU attendance in school areas.
PERBANDINGAN NILAI AKURASI DISTILBERT DAN BERT PADA DATASET ANALISIS SENTIMEN LEMBAGA KURSUS Saputra, Ade Chandra; Saragih, Agus Sehatman; Ronaldo, Deddy
Jurnal Teknologi Informasi: Jurnal Keilmuan dan Aplikasi Bidang Teknik Informatika Vol. 18 No. 2 (2024): Jurnal Teknologi Informasi : Jurnal Keilmuan dan Aplikasi Bidang Teknik Inform
Publisher : Universitas Palangka Raya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47111/jti.v18i2.13278

Abstract

Penelitian ini bertujuan untuk menerapkan Analisis Sentimen dalam Ulasan Kursus dengan menggunakan pendekatan Transfer Learning menggunakan model bahasa DistilBERT dalam konteks pengembangan sistem pendidikan. Dengan pertumbuhan yang pesat dalam domain e-learning dan layanan kursus online, pemahaman pengguna terhadap berbagai kursus menjadi semakin penting bagi institusi pendidikan. Metode transfer learning, yang mengandalkan model-model NLP yang sudah terlatih seperti DistilBERT, telah terbukti efektif dalam tugas analisis sentimen dengan kinerja yang baik dan efisiensi yang tinggi. Dengan peningkatan minat pada pembelajaran online, penelitian ini menginvestigasi bagaimana pendekatan analisis sentimen dapat memberikan wawasan yang lebih dalam terhadap ulasan kursus. Dengan penerapan teknik DistilBERT, diharapkan sistem mampu efektif dalam mengekstrak sentimen yang terkandung dalam ulasan tersebut, memberikan pemahaman menyeluruh terkait pendapat dan perasaan pengguna terhadap kursus yang mereka ikuti. Melalui penelitian ini, diharapkan dapat memberikan kontribusi penting bagi penyelenggara kursus dalam meningkatkan kualitas layanan pendidikan yang mereka tawarkan, memberikan umpan balik yang lebih terperinci dan tepat waktu kepada pengguna. Diharapkan diseminasi hasil penelitian ini memberikan pandangan yang lebih luas mengenai penerapan transfer learning dalam analisis sentimen, terutama dalam konteks ulasan kursus
SISTEM PAKAR APLIKASI DIAGNOSA PENYAKIT DALAM BERBASIS WEBSITE Zahra, Haura; Asyhari, Muhammad Fiddiana; Romansah, Romansah
Jurnal Teknologi Informasi: Jurnal Keilmuan dan Aplikasi Bidang Teknik Informatika Vol. 18 No. 2 (2024): Jurnal Teknologi Informasi : Jurnal Keilmuan dan Aplikasi Bidang Teknik Inform
Publisher : Universitas Palangka Raya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47111/jti.v18i2.14046

Abstract

An internal medicine expert system is an application designed to assist in the process of diagnosing diseases based on symptoms experienced by patients. In today's digital era, the use of web-based expert systems is growing because it provides wider accessibility to users. This research aims to implement a website-based internal medicine expert system as a diagnosis tool for the general public. This system development method involves the steps of needs analysis, system design, implementation, and evaluation. In the needs analysis phase, data on disease symptoms and related medical information were collected to build the expert system knowledge base. Based on the analysis, a user-friendly website interface was designed as well as an algorithm for matching symptoms with corresponding diseases. Implementation was done using web technologies such as HTML, CSS, and JavaScript to build an interactive and responsive user interface. System evaluation was conducted through functionality and diagnosis accuracy trials involving a number of sample disease cases. The evaluation results show that the web-based internal medicine expert system is able to provide a fairly accurate diagnosis according to the symptoms entered by the user. Thus, the implementation of this system can be an effective solution in helping people to make an initial diagnosis of the disease.
IMPLEMENTASI METODE DOUBLE EXPONENTIAL SMOOTHING HOLT PADA PERAMALAN IPM DI KALIMANTAN TIMUR Muhammad Irfan Zaky; Amin Padmo Azam Masa; Islamiyah, Islamiyah
Jurnal Teknologi Informasi: Jurnal Keilmuan dan Aplikasi Bidang Teknik Informatika Vol. 18 No. 2 (2024): Jurnal Teknologi Informasi : Jurnal Keilmuan dan Aplikasi Bidang Teknik Inform
Publisher : Universitas Palangka Raya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47111/jti.v18i2.14321

Abstract

Human Development Index (HDI) is a human development measuring tool introduced by the UNDP in 1990. Based on data from the East Kalimantan Central Statistics Agency (BPS), since 2010, the East Kalimantan Province HDI has experienced an upward trend with a decline in 2020 due to pandemic . In 2022, East Kalimantan Central Statistics Agency recorded the HDI value for East Kalimantan as reaching 77.44. This achievement makes East Kalimantan Province a province with a high level of human development and the 3rd ranked province with the highest HDI nationally. Based on this ranking, there is still room for the government to increase the HDI value of East Kalimantan to prepare it to become the new capital of the country. Therefore, a forecast is needed to find out the HDI value of East Kalimantan Province for the next few years as a consideration for the government in making policies. One forecasting method that can be used to predict the HDI of East Kalimantan Province is Holt's Double Exponential Smoothing (DES). The results obtained from DES Holt forecasting of HDI data for East Kalimantan Province for 2023-2027 are 77.95; 78.46; 78.98; 79.49; 80.01 with a Mean Absolute Percentage Error value of 0.229%.
PREDIKSI HARGA BERAS PREMIUM TAHUN 2024 MENGGUNAKAN METODE GRADIENT BOOSTED TREES REGRESSION Andriyani , Mayrisa; Nurwilda , Siti; Haq, Dina Zatusiva; Novitasari, Dian C Rini
Jurnal Teknologi Informasi: Jurnal Keilmuan dan Aplikasi Bidang Teknik Informatika Vol. 18 No. 2 (2024): Jurnal Teknologi Informasi : Jurnal Keilmuan dan Aplikasi Bidang Teknik Inform
Publisher : Universitas Palangka Raya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47111/jti.v18i2.14859

Abstract

Food needs are a special concern among the community. Every year the growth of Indonesian society increases so that the amount of food needed increases, especially rice which is the staple food of Indonesian society. Regarding this, the public needs information regarding forecasting rice prices for future needs. Therefore, this research aims to predict rice prices using the Gradient Boosted Trees Regression method. This method was chosen because of its ability to produce accurate predictions by minimizing errors through an ensemble approach. Evaluation is seen from the R-Squared and Root Mean Square Error (RMSE) values. The results of research using the Gradient Booster Trees Regression model obtained an R-Squared value of 0.9047 and an RMSE value of 0.0473, which indicates that the model has a high level of accuracy in predicting rice prices. The results of the dataset testing are divided into 80 percent training data and 20 percent for testing data. Based on this research, model testing was carried out by displaying decision tree visualization, using a sample of 50 decision trees.
ARIMA (AUTOREGRESSIVE INTEGRATED MOVING AVERAGE) SEBAGAI ALGORITMA PREDIKSI TINGKAT PEMBATALAN HEREGISTRASI MAHASISWA BARU DI UNIVERSITAS X Rahmawati, Verra Sri Yulia; Setiawan, Iwan Rizal; Asriyanik, Asriyanik
Jurnal Teknologi Informasi: Jurnal Keilmuan dan Aplikasi Bidang Teknik Informatika Vol. 18 No. 2 (2024): Jurnal Teknologi Informasi : Jurnal Keilmuan dan Aplikasi Bidang Teknik Inform
Publisher : Universitas Palangka Raya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47111/jti.v18i2.14913

Abstract

The digitalization era has transformed education, impacting new student admissions in Indonesia, which has various universities: State, Official, Religious, and Private. These universities share common procedures for admitting new students: file selection, test selection, and re-registration. However, many new students cancel their re-registration due to financial constraints, distance, or choosing another campus. Some students neglect the re-registration process until the deadline passes, affecting the accreditation of study programs and the reputation of the campus. To address this issue, a prediction model for re-registration cancellation rates can evaluate campus performance in attracting new students. The ARIMA algorithm (AutoRegressive Integrated Moving Average) is proposed as a suitable prediction model for time series data. This model can help universities identify and address factors leading to re-registration cancellations, thereby improving their performance and reputation. Using the SEMMA (Sample, Explore, Modify, Model, Assess) data mining methodology, the research produced an evaluation matrix with RMSE (Root Mean Square Error) values for various features: "non_registered" (145.77), "parents_income" (0.84), "parents_job" (4.07), and "entrance" (0.16). Additionally, the correlation matrix revealed two variables with a high influence on the target: "entrance" (0.85) and "parents_income" (0.68).

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