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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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METODE PEMBOBOTAN TF-IDF UNTUK KLASIFIKASI TEKS QUICK COUNT PEMILIHAN WAKIL PRESIDEN INDONESIA 2024 PADA X TWITTER DENGAN METODE SVM Pranata, Ricky Albin; Rudiman, Rudiman; Azmi Verdikha, Naufal
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.v18i2.14934

Abstract

The 2024 Indonesian Vice Presidential Election Quick Count sparked diverse public reactions on X Twitter. The sheer volume and variety of expressed opinions complicate accurate sentiment identification and classification. This study aims to develop a text classification model using Support Vector Machine (SVM) to identify sentiment in election Quick Count-related tweets. Data was acquired through tweet collection, followed by pre-processing, word weighting using TF-IDF, and data splitting for model training and testing. Results indicated that the developed SVM model achieved 77.30% accuracy in tweet sentiment classification. The model's implementation is expected to aid in more effective information filtering and assist stakeholders in understanding public opinion more accurately.
ANALISIS PENYAKIT PADA TUMBUHAN HIDROPONIK SELADA MENGGUNAKAN METODE FORWARD CHAINING Huda, Khoirul Huda Dwi Putra; Arbansyah, Arbansyah; Yulianto, Fendy
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.v18i2.14957

Abstract

This research, titled "Analysis of Diseases in Hydroponic Lettuce Plants Using the Forward Chaining Method," focuses on the process of identifying diseases in hydroponic lettuce plants through an expert system. Hydroponic lettuce plants can be affected by various diseases such as soft root, root rot, yellowing leaves, and others. Therefore, there is a need to facilitate farmers and laypeople in detecting diseases in hydroponic lettuce plants and easily identifying them by simply answering diagnostic questions about the disease symptoms. This research develops the results of the disease analysis in hydroponic lettuce plants using the Forward Chaining method through an expert system. The Forward Chaining method is used due to its high effectiveness and accuracy in identifying diseases through IF-THEN Rule s by finding facts from the established Rule s. The data presented includes disease data and symptom data obtained from hydroponic lettuce cultivation on Jalan Muang RT 47 Lempake. This research involves data collection, data analysis, and BlackBox testing. The development of the website for analyzing diseases in hydroponic lettuce plants using the Forward Chaining method employs PHP, HTML, CSS, and MySQL programming languages. The results of this research are satisfactory because the Forward Chaining method can accurately detect diseases, and the website runs smoothly and also got an accuracy of 79,16% on the calculation system using the website.
PERBANDINGAN METODE K–NEAREST NEIGHBOR (KNN) DAN NAIVE BAYES TERHADAP ANALISIS SENTIMEN PADA PENGGUNA E-WALLET APLIKASI DANA MENGGUNAKAN FITUR EKSTRAKSI TF-IDF Rayhan, Muhammad Rayhan Elfansyah; Rudiman, Rudiman; Fendy, Fendy Yulianto
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.v18i2.15009

Abstract

This research compares the accuracy of the K-Nearest Neighbor (KNN) and Naive Bayes methods in classifying user sentiment towards the DANA e-wallet application using Term Frequency-Inverse Document Frequency (TF-IDF) feature extraction. User review data was collected through web scraping techniques and labeled by linguists and lexicon models. After undergoing pre-processing steps such as case folding, cleaning, tokenizing, stopword removal, and stemming, the data was classified using the KNN and Naive Bayes methods. The research results indicate that data labeling by linguists significantly improves the accuracy of both classification methods. Additionally, using TF-IDF as a word weighting method proves effective in enhancing the performance of sentiment classification models. Sentiment analysis of user reviews of the DANA application reveals various complaints and issues faced by users, providing information that can be used to improve the features and services offered, thereby increasing user satisfaction. This research also provides a comparison between the KNN and Naive Bayes methods, which can serve as a reference for other researchers in selecting appropriate methods for sentiment analysis on similar datasets.
ANALISIS SENTIMEN PADA ULASAN APLIKASI GOOGLE MAPS TERHADAP PELAYANAN BADAN PENYELENGGARA JAMINAN SOSIAL (BPJS) KESEHATAN SAMARINDA MENGGUNAKAN METODE K-NEAREST NEIGHBOR DENGAN FITUR EKSTRAKSI TF-IDF Ikhsan, Ikhsan Nuttakwa Takbirata Ihram Nabawi; Rudiman, Rudiman; Fendy, Fendy Yulianto
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.v18i2.15010

Abstract

This study aims to analyze public sentiment towards the services of BPJS Kesehatan Samarinda based on reviews on the Google Maps application. The method used in this research is K-Nearest Neighbor (KNN) with TF-IDF (Term Frequency-Inverse Document Frequency) feature extraction. The data used consists of 500 Indonesian-language reviews collected through web scraping techniques. After the data collection process, the data was labeled by an expert, and then a pre-processing stage was carried out, including case folding, cleaning, tokenizing, stop word removal, and stemming. The data was then weighted using the TF-IDF method to identify important words. The testing was conducted using a training and testing data ratio of 70:30 and a k value of 5. The results showed that the KNN method was able to classify positive and negative sentiments with an accuracy rate of 93.3%. This analysis provides an overview of the service quality of BPJS Kesehatan in Samarinda and can be used as a basis for service improvements. Additionally, this research contributes to the use of KNN and TF-IDF for sentiment analysis, opening opportunities for further research in this field.
KLASIFIKASI SENTIMEN X-TWITTER PERIHAL PEMINDAHAN IBU KOTA INDONESIA MENGGUNAKAN EKSTRAKSI FITUR TF-IDF DAN METODE SUPPORT VECTOR MACHINE (SVM) Wahyudi, Tri; Rudiman, Rudiman; Verdikha, Naufal Azmi
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.v18i2.15015

Abstract

The classification model has reached the realm of sentiment classification to analyze user sentiment in providing comments. this research aims to classify sentiment regarding the topic of moving the capital city of Indonesia using the Support Vector Machine (SVM) method with TF-IDF weighting. SVM has its own advantages, namely to overcome complex problems in SVM classification using the kernel function. the kernel functions to transform input data into a high dimensional feature space, allowing linear separation of data more easily. there are 3 sentiment categories in this study, namely Negative, Neutral and Positive sentiment. to determine these 3 categories, researchers used expert labelling services. the purpose of this study using the SVM method and TF-IDF feature extraction is to find out and analyze the accuracy results obtained in processing sentiment data regarding the transfer of the capital city of Indonesia. The accuracy results obtained are 64%, this shows that the SVM method with TF-IDF weighting is able to classify sentiment data with fairly good results.
INFRASTRUKTUR JARINGAN KOMPUTER BERBASIS CISCO PACKET TRACER DI LABKOM UNIVERSITAS MUHAMMADIYAH SUKABUMI Afwa, Nafilah; Dadan Rahmat
Jurnal Teknologi Informasi: Jurnal Keilmuan dan Aplikasi Bidang Teknik Informatika Vol. 19 No. 1 (2025): 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.v19i1.15028

Abstract

Computer network technology plays a crucial role in supporting teaching and learning activities. A Local Area Network (LAN) is a type of computer network that connects devices within a specific area. The application of network technology supports the learning process, including in the Computer Laboratory of the Information Technology Education Program. This research aims to design and configure the computer network infrastructure in the laboratory using wired media. The method used is the Network Development Life Cycle (NDLC), which relies on previous development processes. The research methodology involves simulation with Cisco Packet Tracer to design network topology and configure devices. The research results include the design of computer network infrastructure using wired media and optimal IP configuration. Findings indicate that the devices within the designed network topology are interconnected and can access the internet effectively.
PERBANDINGAN ALGORITMA LOGISTIC REGRESSION DAN ADAPTIVE BOOSTING (ADABOOST) DALAM KLASIFIKASI PENYAKIT GAGAL JANTUNG Anita Desiani; Amran, Ali; Andriani, Yuli; Wahyuni, Tri; Rizki, Fatur
Jurnal Teknologi Informasi: Jurnal Keilmuan dan Aplikasi Bidang Teknik Informatika Vol. 19 No. 1 (2025): Jurnal Teknologi Informasi : Jurnal Keilmuan dan Aplikasi Bidang Teknik Inform
Publisher : Universitas Palangka Raya

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

Abstract

The heart is a vital organ in the human body, responsible for pumping blood throughout the body via the circulatory system. The heart is responsible for the delivery of oxygen and nutrients to tissues, as well as the removal of carbon dioxide and other waste products. Any disruption to the heart's functioning has the potential to be fatal to human survival. One such disruption is heart failure disease, also known as congestive heart failure (CHF). It is of the utmost importance to detect heart failure at an early stage. The early detection of heart failure disease can be achieved through the utilisation of machine learning, which can mitigate the low probability of this disease. This research employs a machine learning system based on artificial intelligence, utilising logistic regression and adaptive boosting (adaboost) algorithms. The research findings indicate that the classification of heart failure can be accurately determined using a range of parameters. The highest accuracy results, derived from this study, are 90% accuracy, 84% precision, 88% recall, and 88% F1-score. These results are exclusively attributable to the adaboost algorithm. In comparison to the logistic regression algorithm, the resulting accuracy is still below that of the adaboost algorithm, with the results being 86% accuracy, 76% precision, 79% recall, and 88% F1-score. It can therefore be concluded that the adaboost algorithm is more effective than the logistic regression algorithm in classifying heart failure disease. This is particularly the case when the selected data set exhibits an unbalanced number of labels.
PREDIKSI EMOSI DALAM TEKS BAHASA INDONESIA MENGGUNAKAN MODEL INDOBERT Saputra, Ade Chandra; Saragih, Agus Sehatman; Ronaldo, Deddy
Jurnal Teknologi Informasi: Jurnal Keilmuan dan Aplikasi Bidang Teknik Informatika Vol. 19 No. 1 (2025): 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.v19i1.17617

Abstract

This study aims to predict emotions in Indonesian text using the IndoBERT model. Emotions play an essential role in human communication and have a significant impact on sentiment analysis and natural language processing. In Indonesia, the lack of optimized datasets and models for emotion analysis in the Indonesian language poses a major challenge. This research utilizes IndoBERT, a BERT-based model specifically trained for Indonesian, to predict six categories of emotions: anger, sadness, happiness, love, fear, and disgust. The research methodology includes data collection from social media X, data preprocessing, emotion labeling, model training, and performance evaluation using metrics such as accuracy, precision, recall, and F1-score. Results show an overall model accuracy of 73%, with strong performance in recognizing emotions like "disgust" and "fear," although there are misclassifications in distinguishing similar emotions like "happiness" and "love." These findings indicate that IndoBERT has significant potential for emotion prediction in the Indonesian language and provides a foundation for developing more culturally relevant NLP technologies for Indonesia.
IMPLEMENTASI SISTEM INFORMASI DESA BERBASIS SMART VILLAGE DALAM MENDUKUNG TRANSFORMASI DIGITAL DI PEDESAAN STUDI KASUS: DESA SUKADAMAI Wardhani, Muhammad; Hidayatunnisa'i; Mawansyah, Julfikar
Jurnal Teknologi Informasi: Jurnal Keilmuan dan Aplikasi Bidang Teknik Informatika Vol. 19 No. 1 (2025): 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.v19i1.18702

Abstract

Sukadamai Village is located in Manggelewa Sub-district, Dompu Regency, West Nusa Tenggara Province. Sukadamai Village is a remote area situated far from the city center, making administrative services an essential aspect for the community. However, several issues are associated with the management of population data, particularly in administrative processes. The data management is entirely conducted manually, utilizing physical record books and printed office media. Moreover, villagers must visit the village office in person to request information and complete administrative tasks. To address these challenges, the authors developed a web-based village information system that supports the concept of a smart village. This system aims to assist the local government and surrounding community in streamlining administrative tasks and ensuring consistent information dissemination. Based on testing, the system received an 89% approval index, with 30 respondents stating they "STRONGLY AGREE" on the implementation of an Android-based village information system. The objective of this study is to leverage technological advancements to improve administrative efficiency. The trial results indicate that the system is effective and applicable in real-world scenarios.
SISTEM KEAMANAN JARINGAN KOMPUTER BERDASARKAN AHLI FORENSIK Adawiah, Robiatul; Jalu Muhammad Abror
Jurnal Teknologi Informasi: Jurnal Keilmuan dan Aplikasi Bidang Teknik Informatika Vol. 19 No. 1 (2025): Jurnal Teknologi Informasi : Jurnal Keilmuan dan Aplikasi Bidang Teknik Inform
Publisher : Universitas Palangka Raya

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

Abstract

Perkembangan teknologi yang semakin pesat dalam jaringan komputer membuat meningkatnya kebutuhan akses jaringan untuk memudahkan pekerjaan seperti kualitas jaringan yang stabil, cepat, dan efisien serta keamanan jaringan yang terkendal, Forensik Jaringan seperti detektif komputer. Forensik membantu mencari tahu siapa yang mencoba merusak jaringan komputer dengan melihat petunjuk dan informasi dari rekaman komputer. Salah satu cara seseorang menyerang komputer disebut serangan Distributed Denial of Service (DDoS). Hal ini terjadi ketika banyak komputer mengirim terlalu banyak permintaan ke satu komputer, sehingga komputer tersebut sangat sibuk sehingga tidak dapat membantu orang lain. Di Universitas Ahmad Dahlan di Yogyakarta, para peneliti mempelajari cara menemukan serangan ini. Mereka menggunakan perangkat lunak khusus yang disebut Winbox RouterOS untuk melihat hal-hal seperti siapa yang menyerang, berapa banyak permintaan data yang dikirim, dan kapan serangan terjadi. Mereka juga menguji sistem keamanan komputer mereka dengan program lain yang disebut LOIC untuk melihat seberapa baik sistem tersebut dapat melindungi dari serangan DDoS.[1]

Page 45 of 47 | Total Record : 465

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