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Identifikasi Penyakit Daun pada Tanaman Padi Menggunakan Ekstraksi Fitur Gray Level Co-occurrence Matrix (GLCM) dan Metode K-Nearest Neighbour (KNN) Rustanto, Diki Wahyudi; Liantoni, Febri; Prakisya, Nurcahya Pradana Taufik
JUSTIN (Jurnal Sistem dan Teknologi Informasi) Vol 12, No 1 (2024)
Publisher : Jurusan Informatika Universitas Tanjungpura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26418/justin.v12i1.69752

Negara Indonesia merupakan salah satu negara agrikultur, di mana bidang pertanian berperan penting dalam menjaga keberlangsungan hidup. Hal ini dikarenakan, sebagian besar masyarakat Indonesia menggunakan beras sebagai bahan pangan pokok mereka. Sedangkan ketersedian bahan pangan pokok masyarakat sudah berkurang, karena adanya alih fungsi lahan-lahan pertanian menjadi perumahan, industry, dan lain-lain. Bukan hanya itu saja, permasalahan lain yang dapat menurunkan ketersediaan bahan pangan yaitu seperti, kondisi iklim atau cuaca, system pengairan, serangan hama dan masih banyak lagi permasalahan yang dapat mengakibatkan panen menjadi kurang maksimal. Penggunaan teknologi dalam bidang pertanian seharusnya menjadi lebih mudah dan membantu para petani dalam mendeteksi penyakit yang menyerang daun padi. Karena itu, deteksi dan klasifikasi hama pada daun padi perlu dilakukan untuk mengevaluasi akurasi, presisi, dan recall menggunakan perhitungan matriks kebingungan (confusion matrix) dengan menerapkan algoritma K-Nearest Neighbors (KNN). Dari hasil klasifikasi tersebut menghasilkan nilai akhir akurasi paling tinggi yaitu sebesar 73% pada jarak piksel (d) yaitu 5 dan nilai tetangga (k) yaitu 3 pada offset 0 °. Hal ini menunjukkan bahwa algoritma KNN cukup baik dalam melakukan klasifikasi.

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

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.

Pemanfaatan Metode Simple Additive Weighting dalam Sistem Pendukung Keputusan untuk Menentukan Siswa Berprestasi Nurulita, Khiara; Prakisya, Nurcahya Pradana Taufik; Maryono, Dwi
JIPTEK: Jurnal Ilmiah Pendidikan Teknik dan Kejuruan Vol 17, No 2 (2024): July
Publisher : Faculty of Teacher Training and Education Universitas Sebelas Maret Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20961/jiptek.v17i2.76248

Pemberian penghargaan kepada siswa berprestasi adalah salah satu pendorong semangat agar siswa mengembangkan, meningkatkan, dan mempertahankan prestasinya dibidang akademik/non akademik sehingga timbul persaingan sehat antar siswa dalam hal positif. Namun, dalam penentuan siswa berprestasi faktor penentu hanya berpacu pada hasil nilai ujian nasional. Kekurangannya adalah dinilai subyektif karena siswa yang memiliki prestasi belum tentu mandapatkan nilai ujian baik sehingga dirancanglah sebuah sistem yang dapat membantu dalam penentuan siswa berprestasi yaitu Sistem Pendukung Keputusan (SPK). Penelitian ini menggunakan Systems Development Life Cycle model prototyping dan jenis penelitian research and development. Pengumpulan data menggunakan metode wawancara, studi pustaka, dan angket. Pengujian sistem dengan blackbox testing, penilaian sistem ahli media dan evaluasi sistem menggunakan metode System Usability Scale (SUS). Hasil penelitian menunjukkan bahwa sistem sangat layak dalam penggunaan dan pengembangan menentukan siswa berprestasi dengan hasil efektif dan objektif. Hal tersebut dilatarbelakangi dari perolehan nilai rata-rata ahli media sebesar 88% dan evaluasi sistem oleh user sebesar 83.75. Dikarenakan nilai berada pada kisaran 81-100, sistem dapat dikategorikan sangat layak digunakan dan dikembangkan.

Pengembangan Mobile Learning Platform Pemrograman Dasar Python dengan Menggunakan Pyscript untuk Siswa Sekolah Menengah Kejuruan Sidauruk, Deardo Satria Ristiawan; Prakisya, Nurcahya Pradana Taufik; Hatta, Puspanda
JUSTIN (Jurnal Sistem dan Teknologi Informasi) Vol 13, No 1 (2025)
Publisher : Jurusan Informatika Universitas Tanjungpura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26418/justin.v13i1.84813

Pemerataan fasilitas pendidikan di Indonesia masih menjadi isu yang krusial. Salah satu proses belajar mengajar yang terdampak dari masalah ini adalah pembelajaran pemrograman dasar untuk siswa SMK/sederajat. Mata pelajaran ini seringkali menitikberatkan pada penggunaan komputer atau laptop agar proses belajar dapat berjalan dengan baik. Baru-baru ini, telah dikembangkan teknologi baru untuk menjalankan Python live compiler dalam web HTML dengan menggunakan PyScript. Penelitian ini bertujuan untuk menguji pemanfaatan PyScript dalam pengembangan media pembelajaran mobile berbasis web dengan pokok materi pemrograman dasar Python. Pengembangan media pembelajaran dilakukan menggunakan model pengembangan SAM (Successive Approximate Model). Model SAM diterapkan melalui tiga tahap utama: persiapan, perancangan iteratif, dan pengembangan iteratif. Pada tahap persiapan, kebutuhan pengguna dan perangkat dirumuskan, diikuti dengan desain prototipe awal. Pada tahap perancangan iteratif dilakukan pengujian dan revisi berulang, serta validasi oleh ahli. Tahap akhir adalah pengujian produk akhir berbasis web dalam pembelajaran di sekolah. Penilaian dilihat dari aspek penerimaan pengguna, dan dilakukan menggunakan kuesioner WAMMI (Website Analysis and Measurement Inventory). Pengujian penerimaan pengguna dilakukan pada siswa kelas XI SMK Negeri 2 Surakarta, dan didapati hasil tingkat penerimaan pengguna sebesar 78,38%. Hasil ini menunjukkan bahwa media pembelajaran berbasis web ini dapat diterima dengan baik oleh siswa, dan PyScript dapat digunakan sebagai solusi pembelajaran pemrograman yang bersifat mobile, dapat diakses kapan saja dan dimana saja.

Comparative Analysis of Google Vision OCR with Tesseract on Newspaper Text Recognition Prakisya, Nurcahya Pradana Taufik; Kusmanto, Bintang Timur; Hatta, Puspanda
Media of Computer Science Vol. 1 No. 1 (2024): June 2024
Publisher : CV. Digital Innovation

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.69616/mcs.v1i1.178

Optical Character Recognition (OCR) is a technique used to convert image files into machine-readable text. There are two Optical Character Recognition (OCR) algorithms that are currently well known and widely used, namely Google Vision's Optical Character Recognition (OCR) and Tesseract. The purpose of this study is to compare the Optical Character Recognition (OCR) algorithms of Google Vision and Tesseract so that people can more easily find out which algorithm is the right one to implement on the system they are going to build. The method used in this research is Research and Development (R&D) with the stages of literature study, needs analysis, dataset collection and expansion, architectural design development and application modeling, system implementation, testing and evaluation, drawing conclusions. To be able to determine the level of accuracy, precision and sensitivity of each algorithm, this research uses the Confusion Matrix formula. The results of this study conclude that Google Vision's Optical Character Recognition (OCR) algorithm is superior to Tesseract because the level of accuracy, sensitivity, and precision is superior to Google Vision.

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

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.

Development of Information System using Scrum Model : (Case study: Al-Muayyad Windan Islamic Boarding School) Fadliansah, Arafik Nur; Prakisya, Nurcahya Pradana Taufik; Aristyagama, Yusfia Hafid; Jimsan
Jurnal Media Informasi Teknologi Vol. 2 No. 2 (2025): Oktober 2025
Publisher : Digital Innovation

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.69616/mit.v2i2.228

Islamic boarding schools (pesantren) are Islamic educational institutions in Indonesia that must adapt to technological developments. Developing a digital-based management information system is one of the solutions to support effective data management and administrative processes. However, data collection at Al-Muayyad Windan Islamic Boarding School is still carried out conventionally, which often causes inefficiency in managing student information. Therefore, this research aims to develop a student management information system using an agile software development methodology, namely the Scrum model, which allows iterative development and continuous feedback. The implementation of Scrum was successfully carried out through stages including Product Backlog, Sprint Planning, Sprint Backlog, Sprint, Sprint Review, and Retrospective. The resulting system has passed feasibility testing and usability evaluation. The SUS test involving 10 respondents produced an average usability score of 73, categorized as Acceptable on the Acceptability Range, Grade C on the Grade Scale, and Good on the Adjective Rating. These results indicate that the developed system is functionally feasible, user-friendly, and capable of supporting digital transformation in Islamic boarding school management.

Design and Development of an Android-Based Interactive Learning Media for Grade VIII Statistics using the ADDIE Model Rahayu, Lestari; Prakisya, Nurcahya Pradana Taufik; Hatta, Puspanda; Wafa, Alfian Fawaidil; Herianto, Tulus Joseph
Jurnal Media Informasi Teknologi Vol. 2 No. 2 (2025): Oktober 2025
Publisher : Digital Innovation

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.69616/mit.v2i2.236

In the 21st-century learning era, students in several regions of Indonesia still use conventional media such as whiteboards and textbooks in mathematics education. This leads to student boredom and relatively low learning outcomes because they are more interested in playing with smartphones rather than studying. The solution to this problem is to utilize Information and Communication Technology (ICT) as a learning medium. The purpose of this research is to develop, evaluate the feasibility, and determine user perceptions of an interactive Android-based learning media for Grade VIII statistics in junior high school. This research follows a research and development design consisting of three stages: development using the ADDIE method, feasibility analysis, and user perception analysis. The result of the development stage is a valid interactive Android-based learning media. The feasibility analysis results indicate that the media is highly feasible with a rating of 92.5% from media experts and 95% from subject matter experts. The user perception analysis shows that 84.4% of students strongly agree with the usefulness aspect of this media, and 89.5% of students strongly agree with the ease of use aspect. Therefore, it can be concluded that this interactive learning media is worthy, user-friendly, and beneficial in the learning process.

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

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.

Faster R-CNN implementation for hand sign recognition of the Indonesian sign language system (SIBI) Adhiatma, Paulus Lestyo; Prakisya, Nurcahya Pradana Taufik; Ariyuana, Rosihan
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 6: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i6.pp5759-5769

The Indonesian sign language system (SIBI) is the authorized sign system in Indonesia that the deaf society uses to convey in Indonesian. However, its use still needs to be expanded and more widespread in the community, causing difficulties in communication for hard-of-hearing people. The product of deep learning technologies such as faster region-based convolutional neural network (Faster R-CNN) in object recognition has the potential to help improve communication between deaf people and the general public. This research will implement the Faster R-CNN algorithm with three different residual network (ResNet) architectures (50, 101, and 152) for SIBI recognition. The comparison of the faster R-CNN algorithm with different architectures is also conducted to identify the best architecture for SIBI recognition, and the results are evaluated using accuracy, precision, recall, and F1-score metrics from confusion matrix calculation and execution time. Faster R-CNN model with ResNet-50 architecture showed the best and most efficient performance with accuracy, recall, precision, and F1-score metrics of 96.15%, 95%, 93%, and 94%, respectively, and an execution time of 36.84 seconds in the testing process compared to models with ResNet-101 and ResNet-152 architectures.

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