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All Journal Jurnal Sains dan Teknologi Jurnal Simetris Jurnal Buana Informatika Bulletin of Electrical Engineering and Informatics Jurnal Simantec Jurnal Teknologi Register: Jurnal Ilmiah Teknologi Sistem Informasi EMITTER International Journal of Engineering Technology JOIN (Jurnal Online Informatika) Jurnal ELTIKOM : Jurnal Teknik Elektro, Teknologi Informasi dan Komputer INTEGER: Journal of Information Technology IJIE (Indonesian Journal of Informatics Education) Informatika Pertanian Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control SINTECH (Science and Information Technology) Journal Prosiding Seminar Nasional Sains dan Teknologi Terapan Jurnal ULTIMATICS Journal of Information Technology and Computer Engineering JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) JISKa (Jurnal Informatika Sunan Kalijaga) Jurnal Mnemonic DEDIKASI: Community Service Reports Journal of Informatics and Vocational Education Journal of Soft Computing Exploration Journal La Edusci JUSTIN (Jurnal Sistem dan Teknologi Informasi) Jurnal Pemberdayaan Masyarakat JGHTT Indonesian Journal of Learning and Instructional Innovation
Febri Liantoni, Febri
Universitas Sebelas Maret
Aerospace Engineering Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Mathematics Mechanical Engineering Physics Social Sciences Other

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Application of Convolutional Neural Network Using TensorFlow as a Learning Medium for Spice Classification Saputro, Muhammad Naufal Adi; Liantoni, Febri; Maryono, Dwi
ULTIMATICS Vol 16 No 1 (2024): Ultimatics : Jurnal Teknik Informatika
Publisher : Faculty of Engineering and Informatics, Universitas Multimedia Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31937/ti.v16i1.3304

Abstract

The purpose of this research are: (1) To determine the accuracy of the CNN method in the development of a website for classifying spices, (2) To assess the feasibility of the spice classification website as a learning medium, (3) To ascertain user responses to the spice classification website as a learning medium. The method employed in this research is research and development. This study utilizes the ADDIE development method, which comprises 5 stages: (1) Analysis, (2) Design, (3) Development, (4) Implementation, and (5) Evaluation. The research yielded a significantly high accuracy rate. This is demonstrated by the results showing an accuracy of 96%, precision of 97%, and recall of 96%. Moreover, the research found the developed website to be feasible. This is supported by the evaluation using the Learning Object Review Instrument (LORI), resulting in a score of 88% from media experts and a score of 90% from subject matter experts. Additionally, user response was positive. This is evidenced by testing the learning media on 10th-grade culinary students from SMK N 4 Surakarta, which yielded a score of 76% using the System Usability Scale (SUS), indicating a favorable usability assessment. In conclusion, the spice classification website, as a learning medium, can be employed as a suitable educational tool.
Penggunaan Arnold Cat Map Dan Beta Chaotic Map Pada Enkripsi Data Citra Rahmawati, Weny Mistarika; Liantoni, Febri
Jurnal ELTIKOM : Jurnal Teknik Elektro, Teknologi Informasi dan Komputer Vol. 2 No. 2 (2018)
Publisher : P3M Politeknik Negeri Banjarmasin

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31961/eltikom.v2i2.85

Abstract

Penggunaan citra dalam kehidupan sehari-hari mengalami peningkatan seiring berkembangnya teknologi informasi. Untuk itu diperlukan sebuah cara agar data citra dapat ditransmisikan dengan aman. Salah satunya adalah dengan melakukan enkripsi pada citra. Citra terenkripsi akan membuat citra hanya dapat dibaca oleh pihak yang berwenang saja. Skema yang digunakan pada proses enkripsi dapat berupa permutasi. Pada penelitian ini menggunakan Arnold cat map untuk melakukan permutasi pada enkripsi citra. Namun permutasi saja tidak cukup aman untuk mengenkripsi citra. Citra yang telah dipermutasi selanjutnya ditambah dengan algoritma lain berbasis chaos. Beta chaotic map digunakan dalam penelitian ini karena memiliki parameter yang lebih banyak dibandingkan dengan map jenis lain. Dengan parameter yang lebih besar maka akan memperkuat hasil enkripsi. Hasil pengujian yang dilakukan pada penelitian ini menunjukkan bahwa skema enkripsi memiliki ketahanan terhadap serangan brute force dan serangan analisis histogram. Citra asli akan memiliki bentuk yang sangat berbeda dengan citra hasil enkripsi yang dibuktikan dengan perhitungan nilai NPCR.
Identifikasi Kematangan Buah Pisang Berdasarkan Variasi Jarak Menggunakan Metode K-Nearest Neighbor Ananda, Rizky Putu; Liantoni, Febri; Prakisya , Nurcahya Pradana Taufik
JISKA (Jurnal Informatika Sunan Kalijaga) Vol. 9 No. 3 (2024): September 2024
Publisher : UIN Sunan Kalijaga Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14421/jiska.2024.9.3.159-169

Abstract

This research aims to identify the level of ripeness of kepok bananas based on the color of their skin using the K-Nearest Neighbor (K-NN) method. Bananas are an important commodity in Indonesia, and various ripeness levels need to be identified. The current process of identifying banana ripeness is still done manually, which requires a lot of labor and tends to be subjective. The K-NN method is used to classify bananas based on their skin color. This research involves the collection of banana images with three ripeness levels (raw, ripe, and overripe) and the extraction of RGB color features from these images. Three distance methods, namely Euclidean, Minkowski, and Manhattan, are also employed to compare accuracy results. The evaluation results of this research show that the accuracy value for the Euclidean distance method is 84%, the Minkowski distance method is 82%, and the Manhattan distance method is 80%. Thus, the findings indicate that the K-NN method and the Euclidean distance method provide good results in identifying the ripeness level of bananas. By implementing the K-NN algorithm, this research attempts to address the weaknesses of the time-consuming and subjective manual identification process, with the hope of providing a more accurate and efficient solution for the banana industry. The results of this research can be used to automate the identification process of banana ripeness levels and improve efficiency in banana sorting. It is expected that this research can provide practical benefits to the community and serve as a basis for further research in this field.
Deep Learning dalam Prediksi Kebiasaan Merokok di Inggris Guna Mendukung Kebijakan Kesehatan Masyarakat yang Lebih Efektif Prabaswara, Muhammad Arden; Pratama, Kalistus Haris; Majid, Desva Fitranda; Liantoni, Febri
JISKA (Jurnal Informatika Sunan Kalijaga) Vol. 9 No. 2 (2024): Mei 2024
Publisher : UIN Sunan Kalijaga Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14421/jiska.2024.9.2.105-111

Abstract

Smoking is a common practice throughout the world, where a person smokes and inhales the smoke produced from burning tobacco or other tobacco products. This action has become a significant global health issue because of the various health risks. This activity is often considered an addictive habit because nicotine, the psychoactive compound in tobacco, can cause physical and psychological dependence. This research applies Deep Learning methods to predict data on smoking habits in the UK. The dataset used in this research includes information about gender, age, marital status, highest level of education, nationality, ethnicity, income, and region. Through this research using Deep Learning methods, we can examine a complex data set that describes Smoking Habits in the UK. Based on trials with a dataset of 1,691 items, an accuracy of 78% was obtained. This research can provide important insights into the effectiveness of anti-smoking policies that have been implemented and help plan further actions to reduce the prevalence of smoking and its negative impact on society.
Prediction of PTIK students' study success in the first year using the c4.5 algorithm Astuti, Asri; Maryono, Dwi; Liantoni, Febri
Journal of Soft Computing Exploration Vol. 5 No. 1 (2024): March 2024
Publisher : SHM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joscex.v5i1.237

Abstract

The purpose of this study is to determine the factors that influence the success of student studies in the first year through data mining research using the C4.5 algorithm. This research is a type of quantitative research. This research uses student data of a study program as much as 85 data which will be processed using the Weka application. The data obtained will then be processed using the C4.5 data mining method to produce a decision tree containing rules to predict the success of student studies in the first year. The best result using percentage-split 80% obtained an accuracy of 82.35% as well as the rules contained in the decision tree. The most important factor in determining the success of studies in first-year students is the selection of college entrance pathways. Other factors that become other determinants are education before college, intensity of communication with friends, class year, intensity of off-campus organizations, and plans to change study programs.
Comparative study of marker-based and markerless tracking in augmented reality under variable environmental conditions Sulistiyono, Mulia; Hasyim, Jaka Wardana; Bernadhed, Bernadhed; Liantoni, Febri; Sidauruk, Acihmah
Journal of Soft Computing Exploration Vol. 5 No. 4 (2024): December 2024
Publisher : SHM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joscex.v5i4.503

Abstract

Augmented reality (AR) technology integrates virtual content into real environments using two main methods: marker-based and markerless tracking. Marker-based tracking relies on printed markers for object placement, while markerless uses environmental features for flexibility and accuracy. This research aims to evaluate the combined impact of environmental factors-distance, angle, and lighting-on these two methods. The Multimedia Development Life Cycle (MDLC) methodology was applied by testing 72 combinations of indicators: distance (5-120 cm), angle (30°, 45°, 90°), and light color (red, blue, green, yellow) using Xiaomi Note 8 and Google Pixel 4. Results show markerless tracking is superior in all conditions, achieving a 94.4% success rate on both devices. In contrast, marker-based tracking only achieved 72.2% (Xiaomi Note 8) and 77.8% (Google Pixel 4). Markerless tracking was optimally performed from 50 cm away and up close, while marker-based tracking degraded in performance at long distances and red lighting. Markerless tracking proved to be more reliable and consistent, suitable for dynamic and diverse environments, while marker-based methods remained relevant for short distances and controlled lighting. These findings provide guidance for AR developers in choosing a tracking methodology according to application needs.
Pemanfaatan Teknologi Cloud Computing dalam Pembelajaran Informatika di SMK Purnama, Bayu Rizkhy Candra; Liantoni, Febri; Maryanti
Indonesian Journal of Learning and Instructional Innovation Vol 2 No 01 (2024): Indonesian Journal of Learning and Instructional Innovation: June 2024
Publisher : Universitas Sebelas Maret

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20961/ijolii.v2i01.1306

Abstract

Along with the development of information and communication technology, the use of cloud computing technology in education has become increasingly important. This study aims to explore the impact of using Google Forms, Quizizz, and Wordwall in informatics learning at vocational high schools (SMK). The method used is qualitative, with data obtained through interviews and observations. Observations were conducted when teachers used Google Forms to collect student responses, as well as Quizizz and Wordwall for pretests and post-tests. The results show that the use of cloud computing technology increases the efficiency and effectiveness of learning, as well as the quality of student learning outcomes. The conclusion of this study is that cloud computing technology can have a significant positive impact on informatics learning at SMK. The contribution of this research is to provide a basis for further development and application of digital technology in education.
Increased Mammogram Image Contrast Using Histogram Equalization And Gaussian In The Classification Of Breast Cancer Liantoni, Febri; Sukmagautama, Coana; Myrtha, Risalina
JITCE (Journal of Information Technology and Computer Engineering) Vol. 4 No. 01 (2020)
Publisher : Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/jitce.4.01.40-44.2020

Abstract

Breast cancer is one of the most common diseases among women in several countries. One of the most common methods to diagnose breast cancer is mammography. In this study, we propose a classification study to differentiate benign and malignant breast tumors based on mammogram image. The proposed system includes five major steps, i.e. preprocessing, histogram equalization, convolution, feature extraction, and classification. Image is cropped using region of interest (ROI) at preprocessing stage. In this study, we perform image contrast quality enhancement of the mammogram to view the breast cancer better. Image contrast enhancement uses histogram equalization and Gaussian filter. Gray-Level Co-Occurrence Matrix (GLCM) is used to extract the mammogram features. There are five features used i.e. entropy, correlation, contrast, homogeneity, and variance. The last step is to classify using naïve Bayes classifier (NBC) and k-nearest neighbor (KNN). Based on the hypothesis, the accuracy of NBC method is 90% and the accuracy of KKN method is 87.5%. So, the mammogram image contrast enhancement is well performed.
Penerapan Model Pembelajaran Projek Based Learning dengan Pendekatan STEM terhadap Antusias Siswa Jurusan Akuntansi Fase E pada Mata Pelajaran Informatika di SMK Negeri 3 Surakarta Anas, Rizky Chairul; Liantoni, Febri; Maryanti
Indonesian Journal of Learning and Instructional Innovation Vol 2 No 02 (2024): Indonesian Journal of Learning and Instructional Innovation: December 2024
Publisher : Universitas Sebelas Maret

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20961/ijolii.v2i02.1562

Abstract

Learning is a collaboration between teachers and students to share and process information. The teacher has an crucial role in the learning process as a designer of learning scenarios, so that an interesting teaching and learning process occurs and can increase student enthusiasm. The results of observations found a lack of student enthusiasm in informatics subjects. This is characterized by the lack of interaction between students and teachers, lack of focus on learning, some students are busy playing their own gadgets and the lack of students to ask questions while learning. The learning that took place before was boring using only the lecture and question and answer technique, and the lack of utilization of the facilities provided. This study seeks to explore the implementation of the project-based learning model with a STEM approach in Informatics subjects to gauge student motivation. The utilized research methodology was class action research. The research participants comprised were 35 students of phase E of the Accounting Department at SMK Negeri 3 Surakarta. The data collection method used teacher observation sheets and learning outcomes tests. The observation revealed a high category, reaching a score of 66% in the first meeting, 77% in the second meeting and 77% in the third meeting. These results are also supported by an increase in the class average score by 20%. Based on these findings, it can be iferred that the implementation of the Project Based Learning model with the STEM Approach can increase student enthusiasm.
Clustering Student Competencies Using the K-Means Algorithm Andini, Ratih Friska Dwi; Liantoni, Febri; Budianto, Aris
ULTIMATICS Vol 17 No 1 (2025): Ultimatics : Jurnal Teknik Informatika
Publisher : Faculty of Engineering and Informatics, Universitas Multimedia Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31937/ti.v17i1.4071

Abstract

This study aims to evaluate the effectiveness of the K-Means algorithm in clustering student competencies. The subject of the study is students of the Informatics and Computer Engineering Education study program at a public university in Indonesia, with course score data representing various areas of competence as features. The K-Means algorithm is used to group student data into several clusters based on academic grade patterns. The results show that the K-Means algorithm is quite effective in identifying the initial pattern of student competence, with a Silhouette Score of 0.3489, which falls into the medium category. This study concludes that the use of the K-Means algorithm alone is sufficient to support the analysis of student areas of competence, with potential applications as a recommendation system for students in choosing elective courses and as an evaluation tool for study programs to identify areas of competence that need improvement.
Co-Authors Abin Suarsa Acihmah Sidauruk Agus Efendi Agus Santoso Ananda, Rizky Putu Anas, Rizky Chairul Andini, Ratih Friska Dwi Annisa, Fitri Nur Anwar, Rizak Al Hasbi Aris Budianto, Aris Astuti, Asri Ayuningrum, Erica Devi B.M.A.S. Anaconda Bangkara Bambang Yuwono Basori Basori Bataona, Daniel Silli Bataona, Daniel Silli Belo Ximenes, Jose Feliciano Lim Bernadhed, Bernadhed Chaizara, Rezza Fariszal Hisyam Chastine Fatichah Cucuk Wawan Budiyanto Dwi Maryono Farizi, Muhammad Yusuf Hasyim, Jaka Wardana Hendro Nugroho, Hendro Hidayatulah Himawan Hidayatulloh, A. Nururrochman Jaelani, Muhammad Karim, Rifa'i Abdul Kesuma, Halim Perdana Larasati, Sukma Luky Agus Hermanto, Luky Agus Majid, Desva Fitranda Maruf, Irma Rachmawati Maryanti Mulia Sulistiyono Myrtha, Risalina Nanik Suciati Nugroho, Hendro Nurcahya Pradana Taufik Prakisya Palupi, Dian Exsi Prabaswara, Muhammad Arden Prakisya , Nurcahya Pradana Taufik Prakisya, Nurcahya Pradana Taufik Pratama, Kalistus Haris Purnama, Bayu Rizkhy Candra Puspanda Hatta Putri, Giovanni Anggiesta Putri, Nanditya Vianti Qurin, Milani Tanya Rahmawati, Weny M. Ramadhan, Firdaus Ditio Ramadhan, Raqael Fisabillah Rifki Indra Perwira Rosetya, Septiyawan Rosihan Ari Yuana, Rosihan Ari Rustanto, Diki Wahyudi Safitri, Pratiwi Ajeng Saida Ulfa Salsabila, Zahra Khalila Santoso, Agus Adi Saputro, Muhammad Naufal Adi Setianti, Suci Rhamadani Simanjuntak, Ondihon Siswoyo, Risya Ines Putri Sukmagautama, Coana Sukmaningrum, Agy Hafidzah Wahyudi, Yusuf Rois Weny Mistarika Rahmawati Wihidayat, Endar Suprih Yusfia Hafid Aristyagama