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All Journal Jurnal Ilmu Komputer dan Agri-Informatika Jurnal Informatika dan Teknik Elektro Terapan The IJICS (International Journal of Informatics and Computer Science) Jurnal Manajemen Informatika dan Sistem Informasi HOAQ (High Education of Organization Archive Quality) : Jurnal Teknologi Informasi Jurnal Manajemen Informatika & Teknologi Jurnal Ilmiah Teknik Informatika dan Komunikasi KETIK : Jurnal Informatika
Franki Yusuf Bisilisin
STIKOM Uyelindo Kupang
Agriculture, Biological Sciences & Forestry Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering

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Optimasi K-Means Clustering Menggunakan Particle Swarm Optimization pada Sistem Identifikasi Tumbuhan Obat Berbasis Citra Bisilisin, Franki Yusuf; Herdiyeni, Yeni; Silalahi, Bib Paruhum
Jurnal Ilmu Komputer dan Agri-Informatika Vol 3, No 1 (2014)
Publisher : Departemen Ilmu Komputer IPB

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1151.464 KB)

Abstract

Teknologi identifikasi pada penelitian ini diperlukan untuk mempercepat proses identifikasi spesies tumbuhan obat berupa data citra digital. Penelitian ini membangun sistem identifikasi tumbuhan obat menggunakan teknik clustering. Teknik clustering digunakan untuk mengelompokkan data citra sesuai dengan spesies tumbuhan obat. Penelitian ini bertujuan melakukan optimasi k-means clustering menggunakan metode particle swarm optimization (PSO). Metode PSO digunakan untuk mengatasi kelemahan pada metode clustering tradisional yaitu pemilihan pusat cluster awal dan solusi lokal. Proses ekstraksi fitur menggunakan fuzzy local binary pattern (FLBP) untuk merepresentasikan tekstur dari citra. Implementasi program menggunakan bahasa pemrograman C++. Analisis clustering dilakukan untuk 30 spesies tumbuhan obat yang ada di Indonesia dengan jumlah 48 citra masing-masing spesies. Pengukuran kualitas clustering menggunakan nilai quantization error dan akurasi. Hasil yang diperoleh menunjukkan metode PSO mampu meningkatkan kinerja dari metode k-means clustering dalam proses identifikasi tumbuhan obat.Kata kunci: fuzzy local binary pattern, k-means clustering, particle swarm optimization, tumbuhan obat
PENGELOMPOKKAN JENIS RUMPUT LAUT MENGGUNAKAN FUZZY C-MEANS BERBASIS CITRA Franki Bisilisin Yusuf Bisilisin; Remerta Noni Naatonis
Jurnal Manajemen Informatika dan Sistem Informasi Vol. 4 No. 1 (2021): MISI Januari 2021
Publisher : LPPM STMIK Lombok

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36595/misi.v4i1.212

Abstract

Rumput laut merupakan komoditas unggulan bagi petani rumput laut dalam meningkatkan pendapatan rumah tangga. Rumput laut tersedia dalam jumlah banyak dan berbagai jenis rumput laut banyak ditemukan di perairan Teluk Kupang. Permasalahan yang terjadi adalah minimnya pengetahuan petani rumput laut tentang jenis rumput laut yang tersedia. Teknologi sangat dibutuhkan untuk membantu permasalahan yang dihadapi. Salah satunya adalah pengelompokan jenis rumput laut dengan menggunakan komputer untuk mengidentifikasi jenis rumput laut. Data yang digunakan adalah 10 jenis rumput laut dengan masing-masing 10 jenis diambil di Teluk Kupang. Data citra diekstraksi untuk mendapatkan karakteristik tekstur menggunakan pola fuzzy local binary (FLBP). Pengelompokan jenis rumput laut menggunakan metode fuzzy c-means clustering. Sistem dibangun dengan menggunakan Matlab sebagai bahasa pemrograman. Pengujian menggunakan purity untuk menghitung kemurnian sebuah cluster yang direpresentasikan sebagai anggota cluster yang paling sesuai. Hasil penelitian menunjukkan jumlah citra teridentifikasi sebanyak 63 citra rumput laut. Jenis rumput laut yang teridentifikasi dengan benar adalah jenis ulva reticulate dengan nilai kemurnian 1.
Optimasi K-Means Clustering Menggunakan Particle Swarm Optimization pada Sistem Identifikasi Tumbuhan Obat Berbasis Citra Franki Yusuf Bisilisin; Yeni Herdiyeni; Bib Paruhum Silalahi
Jurnal Ilmu Komputer & Agri-Informatika Vol. 3 No. 1 (2014)
Publisher : Departemen Ilmu Komputer - IPB University

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1151.464 KB) | DOI: 10.29244/jika.3.1.37-46

Abstract

Teknologi identifikasi pada penelitian ini diperlukan untuk mempercepat proses identifikasi spesies tumbuhan obat berupa data citra digital. Penelitian ini membangun sistem identifikasi tumbuhan obat menggunakan teknik clustering. Teknik clustering digunakan untuk mengelompokkan data citra sesuai dengan spesies tumbuhan obat. Penelitian ini bertujuan melakukan optimasi k-means clustering menggunakan metode particle swarm optimization (PSO). Metode PSO digunakan untuk mengatasi kelemahan pada metode clustering tradisional yaitu pemilihan pusat cluster awal dan solusi lokal. Proses ekstraksi fitur menggunakan fuzzy local binary pattern (FLBP) untuk merepresentasikan tekstur dari citra. Implementasi program menggunakan bahasa pemrograman C++. Analisis clustering dilakukan untuk 30 spesies tumbuhan obat yang ada di Indonesia dengan jumlah 48 citra masing-masing spesies. Pengukuran kualitas clustering menggunakan nilai quantization error dan akurasi. Hasil yang diperoleh menunjukkan metode PSO mampu meningkatkan kinerja dari metode k-means clustering dalam proses identifikasi tumbuhan obat.Kata kunci: fuzzy local binary pattern, k-means clustering, particle swarm optimization, tumbuhan obat
Teachable Machine: Real-Time Attendance of Students Based on Open Source System Edwin Ariesto Umbu Malahina; Ryan Peterzon Hadjon; Franki Yusuf Bisilisin
The IJICS (International Journal of Informatics and Computer Science) Vol 6, No 3 (2022): November 2022
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/ijics.v6i3.4928

Abstract

The utilization of open source-based services will be very useful, simplifying and accelerating the process of object recognition and complex computational processes, one of them uses the Teachable Machine service. Identification of student faces in real-time attendance is a case study that will be applied to students to recognize and identify accurately and clearly the presence of students during online / offline lectures, by applying Teachable Machine services that have good algorithms with a machine learning approach that utilizes the Tensorflow.js library where the training data testing uses Convolutional Neural Network (CNN). Of the objects identified, the average accuracy of all classes ranged from 91-100%, with the number of samples for each object class being 23 objects or more. Number of sample images in one class. Clothing, object background and lighting intensity around the image object are also very influential in determining the accuracy value of student face recognition later, so that the use of the tensorflow.js library that implements Convolutional Neural Network (CNN) will be very helpful in facial recognition and influencing factors so that the data entered later needs to be further corrected and improved again, so that the results obtained in implementing the online attendance system have been very helpful in detecting student faces with an average accuracy rate of 91.8%
Klasifikasi Motif Kain Tenun Sabu Raijua Menggunakan Convolutional Neural Network (CNN) Berbasis Citra Dabbo, Paulina; Bisilisin, Franki Yusuf
KETIK : Jurnal Informatika Vol. 1 No. 06 (2024): Juli
Publisher : Faatuatua Media Karya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70404/ketik.v1i06.88

Abstract

Indonesia memiliki sekitar 360 suku bangsa yang tersebar di 17.508 pulau dengan budaya yang beragam. Salah satu suku adalah suku Sabu Raijua yang mendiami Pulau Sawu dan Pulau Raijua di Nusa Tenggara Timur. Suku Sabu Raijua memiliki ciri khas dalam bahasa, adat istiadat, dan kain tenun yang bermotif geometris, flora, dan fauna. Kain tenun Sabu Raijua tidak hanya sekadar pakaian adat, tetapi juga simbol kekayaan budaya dan kreativitas masyarakat setempat. Tetapi tidak semua masyarakat memiliki pemahaman mendalam tentang jenis motif kain tenun yang ada di Sabu Raiju. Oleh karena itu, dibutuhkan sistem yang dapat melakukan klasifikasi jenis motif kain tenun Sabu Raijua. Dalam penelitian ini, dikembangkan suatu sistem yang dapat mengklasifikasi motif kain tenun Sabu Raijua menggunakan metode Convolutional Neural Network (CNN). Data yang digunakan yaitu 10 jenis citra kain tenun Sabu Raijua yang diambil dari masing-masing jenis sebanyak 10 sampel, sehingga totalnya terdapat 100 data citra kain tenun Sabu Raijua. Hasil pengujian menunjukkan akurasi yang baik, mencapai 90% dan akurasi keseluruhan sebesar 85%. Evaluasi ini menunjukkan bahwa model CNN mencapai tingkat akurasi yang tinggi dalam mengklasifikasikan motif kain tenun Sabu Raijua.
SISTEM PENDUKUNG KEPUTUSAN UNTUK PENENTUAN PENERIMA BEASISWA KIP KULIAH MENGGUNAKAN METODE ARAS DAN BORDA COUNT DI STIKOM UYELINDO KUPANG BERBASIS WEB mado, silvester; Bisilisin, Franki Yusuf
Jurnal Informatika dan Teknik Elektro Terapan Vol. 13 No. 3 (2025)
Publisher : Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/jitet.v13i3.6966

Abstract

STIKOM Uyelindo Kupang merupakan perguruan tinggi berbasis teknologi informasi pertama di Nusa Tenggara Timur yang memberikan kesempatan kepada mahasiswa kurang mampu untuk memperoleh Beasiswa KIP Kuliah. Namun, proses seleksi beasiswa yang masih menggunakan spreadsheet dinilai kurang efisien dan berisiko menimbulkan kesalahan dalam pengolahan data. Penelitian ini bertujuan untuk mengembangkan Sistem Pendukung Keputusan (SPK) berbasis web dengan menggunakan metode ARAS dan Borda Count untuk membantu proses seleksi penerima beasiswa. Metode ARAS digunakan untuk menilai alternatif berdasarkan kriteria tertentu, sementara metode Borda Count digunakan untuk merangking hasil penilaian berdasarkan hasil perhitungan ARAS. Hasil penelitian menunjukkan bahwa sistem berhasil menampilkan peringkat alternatif, di mana A1 menjadi alternatif terbaik dengan skor 17, dan A3 menjadi yang terendah dengan skor 1. Pengujian sistem menggunakan metode Mean Absolute Percentage Error (MAPE) menghasilkan nilai error sebesar 17,65% yang termasuk dalam kategori model baik. Sistem ini diharapkan dapat membantu proses seleksi penerima beasiswa secara lebih objektif, cepat, dan akurat, serta mendukung penyaluran beasiswa yang lebih tepat sasaran.
Pemetaan Daerah Potensi Rawan Banjir Berbasis Wengis Mengunakan Metode Weighted Overlay di Kecamatan Malaka Barat Michael Lopez; Franki Yusuf Bisilisin
Jurnal Manajemen Informatika & Teknologi Vol. 5 No. 2 (2025): Oktober : Jurnal Manajemen Informatika & Teknologi
Publisher : LPPM Sekolah Tinggi Ilmu Ekonomi - Studi Ekonomi Modern

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/p2fc1g80

Abstract

Floods are a natural disaster that often occurs in Malaka Barat District, Malaka Regency, East Nusa Tenggara Province, especially due to the overflow of the Benainai River. Every year, floods cause material losses and disrupt community activities. However, until now there has been no clear mapping of areas that are potentially prone to flooding in the region. This study aims to map potential flood-prone areas based on WebGIS using the Weighted Overlay method in Malaka Barat District. This method combines five main parameters, namely elevation, slope, distance from the Benainai River, soil type, and land use, which are weighted based on their level of influence on flood events. The results of the analysis will be visualized in the form of a digital map classified into three levels of vulnerability: low, medium, and high. The WebGIS system developed will facilitate access to information for the community and stakeholders in flood disaster mitigation efforts. This research is expected to be a tool in spatial planning and decision making to reduce the impact of flooding in the area.
Prediksi Penjualan Motor Honda pada Dinamika Motor Kupang menggunakan Autoregressive (AR) Theresia Felisitas Dena; Franki Yusuf Bisilisin
Jurnal Ilmiah Teknik Informatika dan Komunikasi Vol. 5 No. 3 (2025): November: Jurnal Ilmiah Teknik Informatika dan Komunikasi 
Publisher : Lembaga Pengembangan Kinerja Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/juitik.v5i3.1622

Abstract

The motorcycle industry in Indonesia is experiencing rapid growth, marked by the increasing number of two-wheeled vehicles every year. Dealer Dinamika Motor Kupang, also known as PT. Dinamika Sejahtera Motor, is one of the authorized Honda motorcycle dealers in Kupang, East Nusa Tenggara. Dinamika Motor Kupang faces the challenge of predicting market demand in order to design a more effective business strategy. Based on the number of motorcycle sales from 2022-2024, there are increases and decreases in the number of motorcycle sales. Therefore, a system is needed to predict motorcycle sales so that it can determine the sales level in the following months and years. This study aims to make a prediction of Honda motorcycle sales using the Autoregressive (AR) method. This method was chosen because it is able to analyze historical data patterns and provide accurate sales estimates. This study uses Honda motorcycle sales data for the period 2022–2024. Based on the test results, the MAPE value obtained was 22.73% with the normal distribution AR model, and 7.81% with the binomial distribution AR model. These results indicate that the binomial distribution AR model is more optimal in predicting motor sales on Beetle Motor Dynamics.
Sistem Pakar Mendiagnosa Penyakit pada Mentimun Menggunakan Fuzzy Mamdani Claudia Krista Minggu; Franki Yusuf Bisilisin
Jurnal Ilmiah Teknik Informatika dan Komunikasi Vol. 5 No. 3 (2025): November: Jurnal Ilmiah Teknik Informatika dan Komunikasi 
Publisher : Lembaga Pengembangan Kinerja Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/juitik.v5i3.1623

Abstract

Cucumber is a horticultural crop with numerous benefits, both in the food and industrial sectors. Cucumber production in Kupang City has seen a notCucumber is a horticultural crop with numerous benefits, both in the food and industrial sectors. Cucumber production in Kupang City has seen a noticeable decline in recent years. One of the main driving factors is the infestation of plant diseases such as yellowing leaves, powdery mildew, and root rot, which impact crop yields and farmer income. To address this issue, an expert system capable of detecting cucumber plant diseases using Mamdani Fuzzy Logistics (FUZZ). The Mamdani Fuzzy Logistics method was chosen because of its decision-making capabilities and its easy-to-understand linguistic-based rules. This system is designed to enable farmers to detect diseases early, take appropriate mitigation measures, and reduce the risk of crop failure. Implementing this technology is expected to increase efficiency in agricultural management and maintain cucumber production. Testing results on 20 cucumber plant data samples using the Mean Absolute Percentage Error (MAPE) yielded an error of 10%. The results showed that two cucumber plant samples showed diagnostic inconsistencies. With this system, farmers are expected to be able to more quickly identify the types of diseases that endanger cucumber plants and also obtain recommendations for appropriate solutions to handle the problem, thereby being able to maintain agricultural productivity.iceable decline in recent years. One of the main driving factors is the infestation of plant diseases such as yellowing leaves, powdery mildew, and root rot, which impact crop yields and farmer income. To address this issue, an expert system capable of detecting cucumber plant diseases using Mamdani Fuzzy Logistics (FUZZ). The Mamdani Fuzzy Logistics method was chosen because of its decision-making capabilities and its easy-to-understand linguistic-based rules. This system is designed to enable farmers to detect diseases early, take appropriate mitigation measures, and reduce the risk of crop failure. Implementing this technology is expected to increase efficiency in agricultural management and maintain cucumber production. Testing results on 20 cucumber plant data samples using the Mean Absolute Percentage Error (MAPE) yielded an error of 10%. The results showed that two cucumber plant samples showed diagnostic inconsistencies. With this system, farmers are expected to be able to more quickly identify the types of diseases that endanger cucumber plants and also obtain recommendations for appropriate solutions to handle the problem, thereby being able to maintain agricultural productivity.        
Sistem Pakar Mendiagnosa Penyakit pada Cabai Menggunakan Metode Certainty Factor Islahul Fikri; Franki Yusuf Bisilisin
Jurnal Ilmiah Teknik Informatika dan Komunikasi Vol. 5 No. 3 (2025): November: Jurnal Ilmiah Teknik Informatika dan Komunikasi 
Publisher : Lembaga Pengembangan Kinerja Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/juitik.v5i3.1621

Abstract

Various species of Capsicum plants belong to the chili pepper genus, but five main species are most widely cultivated. Chili peppers are primarily grown for their fruits, which are important agricultural commodities. However, chili pepper productivity often declines due to pest and disease attacks. Because of this circumstance, a system that can help farmers promptly and precisely identify and manage plant diseases is required. In order to handle ambiguity in the reasoning process, this work aims to develop an expert system that uses the Certainty Factor (CF) method to identify infections in chili plants. The system is built on a knowledge base obtained from agricultural experts, covering five types of diseases and 18 main symptoms. The application is designed as a desktop-based software with a simple user interface and a maximum of seven selectable symptoms to improve diagnostic accuracy. A 10% error rate was obtained from testing 30 chili plant data samples using the Mean Absolute Percentage Error (MAPE), with three samples exhibiting diagnostic differences. This system is expected to enable farmers to more  rapidly identify the types of diseases affecting chili plants and to obtain appropriate handling recommendations, thereby helping maintain agricultural productivity.
Co-Authors Bib Paruhum Silalahi Claudia Krista Minggu Dabbo, Paulina Edwin Ariesto Umbu Malahina Febiola Hutni Mosa Islahul Fikri mado, silvester Michael Lopez Naatonis, Remerta R. Remerta Noni Naatonis Ryan Peterzon Hadjon Theresia Felisitas Dena