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All Journal Jurnal Sains dan Teknologi Jurnal Simetris Jurnal Buana Informatika 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 Journal of Soft Computing Exploration Journal La Edusci JUSTIN (Jurnal Sistem dan Teknologi Informasi) JGHTT Indonesian Journal of Learning and Instructional Innovation
Febri Liantoni, Febri
Universitas Sebelas Maret
Aerospace Engineering Agriculture, Biological Sciences & Forestry Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Mathematics Mechanical Engineering Social Sciences Other

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Personalizing Student Major Selection through Artificial Neural Network Prediction Models Palupi, Dian Exsi; Liantoni, Febri; Efendi, Agus
Journal La Edusci Vol. 6 No. 2 (2025): Journal La Edusci
Publisher : Newinera Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37899/journallaedusci.v6i2.2312

Abstract

This research focuses on a well-known problem in secondary education that a student may have a hard time in choosing their study major to fit their interest, gifts, and long-term goals. Concentrating on the SAINTEK (science and technology) and SOSHUM (social sciences and humanities) streams of Indonesian high schools, the study will incorporate an Artificial neural network (ANN) to provide an insight into the student preferences based on multidimensional data through modeling and predicting purposes. A 44-item questionnaire was distributed to 205 students of SMA Negeri 1 Karanganom to gather the inputs that involved personal interests, parental influence, career perspective, and psychosocial characteristics. It was trained and validated with Stratified K-Fold Cross Validation that delivered good performance scores of average accuracy at 87% and precision value at 89- 91 recall and an F1-score of 90. In addition to the algorithmic validation, qualitative interviews of some of the students indicated that the prediction of the model corresponds to what these students perceive about their academic leanings. The obtained results argue that ANN systems can be used, not only as an error-free classifier but also as an educational decision-support system that can be used to augment student guidance with personally-tailored, data-driven advice. The proposed study locates ANN in the larger pedagogical mission of responsiveness and student-centered planning as such, the study also advances the new discourse of ethical and effective uses of artificial intelligence in education.
Peramalan Nilai Saham BBCA Melalui Pendekatan Time Series Menggunakan Teknik Exponential Smoothing Liantoni, Febri; Simanjuntak, Ondihon
JISKA (Jurnal Informatika Sunan Kalijaga) Vol. 10 No. 3 (2025): September 2025
Publisher : UIN Sunan Kalijaga Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14421/jiska.2025.10.3.259-266

Abstract

Forecasting stock prices plays a crucial role in shaping investment strategies within the financial market. This article aims to predict the stock prices of Bank Central Asia (BBCA), a prominent entity in the Indonesian banking sector. Employing a time series methodology, this study utilizes the Exponential Smoothing technique to anticipate the fluctuations in BBCA's share prices. Meanwhile, the dataset used is the BBCA share price data from April 2001 to early January 2023. The final error rate in this forecast is 10%.
PEMANFAATAN ALGORITMA SAW PADA SISTEM PENUNJANG KEPUTUSAN UNTUK PENENTUAN STRATEGI BELAJAR PADA ADAPTIVE LEARNING Prakisya, Nurcahya Pradana Taufik; Aristyagama, Yusfia Hafid; Budiyanto, Cucuk Wawan; Hatta, Puspanda; Liantoni, Febri; Yuana, Rosihan Ari; Ramadhan, Raqael Fisabillah
JST (Jurnal Sains dan Teknologi) Vol. 11 No. 2 (2022)
Publisher : Universitas Pendidikan Ganesha

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (575.688 KB) | DOI: 10.23887/jstundiksha.v11i2.45319

Abstract

Pada masa pandemi Covid-19, model pembelajaran adaptif menjadi alternatif pilihan dalam pembelajaran jarak jauh pada pendidikan perguruan tinggi. Permasalahan yang ditemui adalah tidak semua tenaga pendidik siap melakukan penyesuaian dalam menjalankan pembelajaran jarak jauh. Akibatnya, peserta didik mungkin menemukan materi pembelajaran online yang terlalu sederhana, atau malah sangat rumit. Hal ini berakibat pada hasil pembelajaran menjadi kurang maksimal. Penelitian ini bertujuan untuk menciptakan adopsi algoritma SAW dalam sistem penunjang keputusan penentuan strategi pembelajaran adaptif. Jenis penelitian ini merupakan research and development. Sistem dikembangkan dengan model spiral. Data dikumpulkan dengan menggunakan kuesioner yang dilekatkan dalam sistem. Anggota sampel data adalah dosen pengguna sistem. Teknik analisis data menggunakan analisis kuantitatif. Hasil penelitian menunjukkan sistem mendapatkan input data dari angket digital terintegrasi yang menggambarkan kondisi dari masing-masing mahasiswa. Sistem dievaluasi dengan menggunakan System Usability Scale (SUS) untuk menganalisis tingkat persepsi kebergunaan sistem. Melalui sistem ini, tenaga pendidik diharapkan dapat memperoleh rekomendasi perlakuan yang sesuai dengan kondisi mahasiswa sehingga mereka dapat lebih fokus pada penerapan strategi dan substansi pembelajaran.
Effect of information gain on document classification using k-nearest neighbor Perwira, Rifki Indra; Yuwono, Bambang; Siswoyo, Risya Ines Putri; Liantoni, Febri; Himawan, Hidayatulah
Register: Jurnal Ilmiah Teknologi Sistem Informasi Vol 8 No 1 (2022): January
Publisher : Information Systems - Universitas Pesantren Tinggi Darul Ulum

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26594/register.v8i1.2397

Abstract

State universities have a library as a facility to support students’ education and science, which contains various books, journals, and final assignments. An intelligent system for classifying documents is needed to ease library visitors in higher education as a form of service to students. The documents that are in the library are generally the result of research. Various complaints related to the imbalance of data texts and categories based on irrelevant document titles and words that have the ambiguity of meaning when searching for documents are the main reasons for the need for a classification system. This research uses k-Nearest Neighbor (k-NN) to categorize documents based on study interests with information gain features selection to handle unbalanced data and cosine similarity to measure the distance between test and training data. Based on the results of tests conducted with 276 training data, the highest results using the information gain selection feature using 80% training data and 20% test data produce an accuracy of 87.5% with a parameter value of k=5. The highest accuracy results of 92.9% are achieved without information gain feature selection, with the proportion of training data of 90% and 10% test data and parameters k=5, 7, and 9. This paper concludes that without information gain feature selection, the system has better accuracy than using the feature selection because every word in the document title is considered to have an essential role in forming the classification.
The Implementation of QR-Code Technology on Bulak Fish Center Information System Liantoni, Febri; Rosetya, Septiyawan; Rahmawati, Weny M.
JOIN (Jurnal Online Informatika) Vol 3 No 2 (2018)
Publisher : Department of Informatics, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/join.v3i2.239

Abstract

The technology that is currently developing is the use of QR-Code. QR-Code can convey information quickly with the acquisition of fast responses as well. QR-Code can be used on smartphones. Some media and companies widely use the QR-Code in Indonesia. QR-Code is not only used as an application identity but is also used as a means of effective, simple and modern business promotion. Making information systems for Sentra Ikan Bulak Surabaya is done by using QR-Code technology as a means of product promotion in the sales process. Research that has been done can run well and can support and be useful in improving the means of promotion. Traders find it easier to collect data on existing systems. Buyers can access the site and see reviews from other buyers so buyers will feel interested in buying the product.
Design and Development of a Learning Style Identification Application for JPTK Students using the K-Nearest Neighbor Ramadhan, Firdaus Ditio; Liantoni, Febri; Prakisya, Nurcahya Pradana Taufik
ULTIMATICS Vol 15 No 2 (2023): 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.v15i2.3299

Abstract

Learning styles are crucial for all students, as the chosen learning style can greatly assist them in learning. The data source for this research originates from questionnaire results distributed to JPTK students of the 2019-2021 cohorts, which were used to assess the effectiveness of a learning style product on the students' JPTK website. This study employs the K-Nearest Neighbor approach, which utilizes the principle of nearest neighbors to categorize students' learning styles based on provided features. The data used in this research is derived from the website that students use to input information about their preferred learning styles. Various elements, including visual, auditory, and kinesthetic preferences, are present in the questionnaire on the website. Subsequently, the data is processed and fed into a Python K Nearest Neighbor model to predict students' learning styles and nearest neighbors. The evaluation results indicate that the developed classification model achieves a reasonably high accuracy level of 93%, making it a useful tool for effectively and efficiently identifying students' learning styles. It is hoped that implementing this learning style classification model will benefit the field of education. By understanding students' learning styles, educators can create more tailored lesson plans, enhance learning outcomes, and reduce the likelihood of knowledge loss.
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.
The Impact of Implementing Kahoot! & Windows Shopping Learning Model on Learning Interest of Class X DKV1 SMK N 3 Surakarta Students in Basic DKV Subjects Chaizara, Rezza Fariszal Hisyam; Liantoni, Febri; Maryanti
Journal of Global Hospitality and Tourism Technology Vol. 1 No. 1 (2024): Journal of Global Hospitality and Tourism Technology - June
Publisher : Politeknik NEST

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5281/zenodo.12590454

Abstract

This research was conducted to find out the effect of implementing Kahoot! and the Windows Shopping learning model on the learning interest of class X DKV1 SMK N 3 Surakarta students in the Basic DKV subject. This is important because students' interest in learning is one of the determining factors for learning that has been designed to run effectively. The method used in this research is qualitative in the form of questionnaires and interviews with 36 class X DKV1 students. The analysis technique in this research uses descriptive analysis and thematic analysis. After data analysis, it can be concluded that implementing Kahoot! and the Windows Shopping model has a positive influence on students' interest in learning as evidenced by the results of questionnaire analysis & interviews which state that every aspect of interest in learning (Happy, Interested, Attentive, Involved) is fulfilled and gets positive perceptions from all students.
Comparison of Adaptive Ant Colony Optimization for Image Edge Detection of Leaves Bone Structure Liantoni, Febri; Perwira, Rifki Indra; Bataona, Daniel Silli
EMITTER International Journal of Engineering Technology Vol 6 No 2 (2018)
Publisher : Politeknik Elektronika Negeri Surabaya (PENS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (455.566 KB) | DOI: 10.24003/emitter.v6i2.306

Abstract

Leaf bone structure has a characteristic that can be used as a reference in digital image processing. One form of digital image processing is image edge detection. Edge detection is the process of extracting edge information from an image. In this research, Adaptive Ant Colony Optimization algorithm is proposed for edge image detection of leaf bone structure. The Adaptive Ant Colony Optimization method is a modification of Ant Colony Optimization, in which the initial an ant dissemination process is no longer random, but it is done by a pixel placement process that allows for an edge based on the value of the image gradient. As a comparison also performed edge detection using Robert and Sobel method. Based on the experiments performed, Adaptive Ant Colony Optimization algorithm is capable of producing more detailed image edge detection and has thicker borders than others. Keywords: edge detection, ant colony optimization, robert, sobel
Implementation of the C4.5 Algorithm to Predict Student Achievement at SMK Negeri 6 Surakarta Putri, Giovanni Anggiesta; Maryono, Dwi; Liantoni, Febri
IJIE (Indonesian Journal of Informatics Education) Vol 4, No 2 (2020): IJIE (Indonesian Journal of Informatics Education)
Publisher : Universitas Sebelas Maret

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20961/ijie.v4i2.47124

Abstract

Data mining is a knowledge used to get information from multiple data. C.45 Algorithm is one of data mining algorithm to classify data to many categories. Implementation of data mining not only could be used in industrial section but it could be used to in educational section (educational data mining) to help teacher and student improve their learning quality. This research purposed to know the implementation of data mining to predict student achievement from many factors could be effected . The research use Knowledge Discovery in Database method and it would be analyzed by accuration calculated from classify model that be form. Result of the research is the rules that formed by the decision tree and it could predict student achievement . Teacher could use it to give special treatment to student who got not good prediction.
Co-Authors Abin Suarsa Acihmah Sidauruk Agus Efendi Agus Santoso Ananda, Rizky Putu Anas, Rizky Chairul Andini, Ratih Friska Dwi Annisa, Fitri Nur Aris Budianto, Aris Astuti, Asri Ayuningrum, Erica Devi B.M.A.S. Anaconda Bangkara Bambang Yuwono Bataona, Daniel Silli Bataona, Daniel Silli Bernadhed, Bernadhed Chaizara, Rezza Fariszal Hisyam Chastine Fatichah Cucuk Wawan Budiyanto Dwi Maryono Hasyim, Jaka Wardana Hendro Nugroho, Hendro Hidayatulah Himawan Hidayatulloh, A. Nururrochman Luky Agus Hermanto, Luky Agus Majid, Desva Fitranda 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 R. Irma Rachmawati, R. Irma Rahmawati, Weny M. Ramadhan, Firdaus Ditio Ramadhan, Raqael Fisabillah Rifki Indra Perwira Rosetya, Septiyawan Rosihan Ari Yuana, Rosihan Ari Rustanto, Diki Wahyudi Salsabila, Zahra Khalila Santoso, Agus Adi Saputro, Muhammad Naufal Adi Simanjuntak, Ondihon Siswoyo, Risya Ines Putri Sukmagautama, Coana Sukmaningrum, Agy Hafidzah Wahyudi, Yusuf Rois Weny Mistarika Rahmawati Wihidayat, Endar Suprih Yusfia Hafid Aristyagama