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Kaesmetan, Yampi R
STIKOM Uyelindo Kupang
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Digital Image Processing to Detect Sumba Woven Fabric Contour Using Gray Level Co-occurrence Matrix and Self Organizing Map Mone, Bintang Vieshe; Kaesmetan, Yampi R; Meo, Meliana O.
Indonesian Journal of Artificial Intelligence and Data Mining Vol 7, No 1 (2024): March 2024
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/ijaidm.v7i1.28355

Abstract

Sumba woven cloth is one of the cultural heritages of the island of Sumba. Based on its manufacture, the classification process for Sumba woven fabrics is based on the identification of colors or motifs. However, the classification process is not an easy process. In addition to the classification process, the wider community also does not get much information about Sumba woven fabrics clearly, therefore digital image processing technology is needed to build a system that can overcome the problems faced. The image of the Sumba woven fabric sample is converted to grayscale and resized, then segmented using Sobel detection. Then extracted using Gray level co-occurrence matrix (GLCM). After extraction, it will be classified using a Self Organizing Map (SOM). Based on the results of this study, it was concluded that the accuracy of the validation test was 80%, and the program was successful.
Web-Based Junior High School Student Attendance System with Face Recognition Feature using the Prototyping Method Kaesmetan, Yampi R; Rosid, Achmat; Fryonanda, Harfebi
Nusantara Journal of Artificial Intelligence and Information Systems Vol. 1 No. 2 (2025): December
Publisher : Faculty of Engineering and Computer Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47776/nuai.v1i2.1873

Abstract

Technology is increasingly developing and innovating rapidly. Among them is the use of technology in various fields, especially in education. Teachers and students at the school still carry out attendance activities manually, namely with a conventional system that requires data recording for each attendance on paper. This system is vulnerable to damage or loss of data because the attendance results there still use paper. The attendance system that utilizes face recognition technology is the system proposed for the formulation of the problem that will be used for the research. The development method of this research uses the Prototyping method which prioritizes speed and time efficiency so that it is very suitable for use considering the current needs for a system that requires speed and accuracy. The framework used in the development of the system is Codeigniter 4. The process of working on the system is system requirements analysis, display design, coding, and testing. The results of the study are to create a website-based attendance application at SMP Daarus Sa'adah by utilizing face recognition technology which is carried out by auto-detecting faces so that it can facilitate users in carrying out attendance activities accurately and quickly.
IDENTIFIKASI JENIS MANGGA BERDASARKAN CIRI DAUN MENGGUNAKAN METODE CNN Sayyid Ahmad Wisak; Nindy Aulia Safirah; Yampi R Kaesmetan
Jurnal Sistem Informasi dan Informatika (Simika) Vol. 7 No. 2 (2024): Jurnal Sistem Informasi dan Informatika (Simika)
Publisher : Program Studi Sistem Informasi, Universitas Banten Jaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47080/simika.v7i2.3295

Abstract

Mango, originating from India and bearing the scientific name Mangifera indica L, spread to Southeast Asia, including Malaysia and Indonesia. Rich in vitamins A and C, mango boosts immunity and exhibits a wide genetic diversity. From the genetic diversity and types of mango leaves, many people do not understand well about the types of mangoes based on mango leaves. Therefore it is necessary to identify the type of mango based on the leaves so that people can easily understand the type of a mango. The Convolutional Neural Network (CNN) method proves effective in identifying plants based on morphological features. CNN, a development from Multilayer Perceptron (MLP), is employed in testing using Teachable Machine with 60 mango leaf images, divided into 3 classes. Across 4 different classifications, the average confusion matrix shows CNN accuracy at 83.30%, precision at 94.43%, and recall at 88.28%. With CNN, the accuracy in identifying mango leaf characteristics improves.
Ekstrasi Fitur Dan Kontur Pada Kain Tenun Sabu Menggunakan Metode GLCM (Gray Level Co-occurrence Matrix) Sanrina Natalia Evelin Tolan; Abraham Do Hina; Yampi R. Kaesmetan
Mars: Jurnal Teknik Mesin, Industri, Elektro Dan Ilmu Komputer Vol. 2 No. 3 (2024): Juni: Mars: Jurnal Teknik Mesin, Industri, Elektro Dan Ilmu Komputer
Publisher : Asosiasi Riset Teknik Elektro dan Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61132/mars.v2i3.99

Abstract

Sabu woven fabric is one of the cultural heritages of Sabu Island. In addition to being a cultural heritage, Sabu woven fabric is one of the handicrafts that still exist today which is preserved by Sabu women. Based on its manufacture, the classification process of Sabu woven fabric is based on color or motif identification. However, the classification process is not an easy process, because the classification process requires time and experts in the field of Sabu woven fabric. In addition to the classification process, the wider community also does not get much information about Sabu woven fabric clearly, because it is necessary to introduce the type of Sabu woven fabric, so that people can know or recognize the type of Sabu ikat woven fabric based on its type. Digital image processing techniques are utilized to build a system that can overcome the problems faced. Furthermore, image feature extraction will be carried out using gray level co-occurrence matrix (GLCM) with 4 features namely contrast, correlation, energy, and homogeneity with angles of 0°, 45°, 90°, and 135°. Each GLCM feature shows the same value even though the original image is rotated. After image feature extraction, the extracted data will be classified using the TensorFlow library. From these results it can be concluded that the program succeeded in selecting the type of Sabu ikat woven fabric class.
SISTEM PENDUKUNG KEPUTUSAN PENENTUAN DAERAH PRIORITAS PENGENDALIAN DEMAM BERDARAH METODE MULTI ATTRIBUTE UTILITY THEORY (MAUT) Bambali, Achmad Bahrudin; Kaesmetan, Yampi R.
Jurnal Publikasi Manajemen Informatika Vol. 4 No. 3 (2025): JURNAL PUBLIKASI MANAJEMEN INFORMATIKA
Publisher : Lembaga Pengembangan Kinerja Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/jupumi.v4i3.3992

Abstract

Demam Berdarah Dengue (DBD) merupakan penyakit yang penyebarannya semakin meluas di indonesia, terutama kondisi iklim tropis yang mendukung pertumbuhan vector penyebarannya, yakni nyamuk Aedes aegypty dan Aedes albopictus. Kecamatan Oebobo merupakan salah satu wilayah di Kota Kupang yang menunjukan fluktuatif pada kasus DBD yang menandakan bahwa ancaman penyakit ini masih tinggi dan perlu penanganan berkelanjutan. Penelitian ini bertujuan untuk mengembangkan sistem pendukung keputusan berbasis web menggunakan metode Multi Attribute Utility Theory (MAUT) guna menentukan daerah prioritas penanganan DBD. Metode maut dipilih karena kemampuannya dalam menganalisis berbagai alternatif secara kuantitatif dan objektif dengan mempertimbangkan berbagai faktor yang mempengaruhi penyebaran DBD. Sistem diharapkan dapat membantu pihak yang berwenang dalam melakukan pengambilan keputusan yang lebih tepat dan objektif dalam mengidentifikasi daerah prioritas penanganan DBD.
SISTEM REKOMENDASI DESTINASI WISATA DI KABUPATEN TIMOR TENGAH SELATAN MENGGUNAKAN METODE SOM (SELF ORGANIZING MAP) selan, frederikus; Yampi R. Kaesmetan
Jurnal Publikasi Manajemen Informatika Vol. 5 No. 1 (2026): JURNAL PUBLIKASI MANAJEMEN INFORMATIKA
Publisher : Lembaga Pengembangan Kinerja Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/jupumi.v5i1.4659

Abstract

The recommendation system for natural tourism destinations in South Central Timor Regency uses the Self-Organizing Map method. As an area with diverse natural tourism potential but has not been optimally explored, South Central Timor requires an innovative approach to increase the visibility of its tourist destinations. This website-based system integrates various important parameters such as natural characteristics, accessibility, facilities, tourist experience, temporal factors, costs, and sustainability aspects to produce personalized recommendations for tourists. The Self-Organizing Map method was chosen because of its ability to group tourist destinations based on similar characteristics without requiring previous data labels, and can identify hidden patterns that may not be visible in conventional analysis. The Final Project shows that the implementation of this recommendation system not only improves the tourist experience through personalization, but also encourages a more even distribution of visits to various tourist destinations in South Central Timor, supports the local economy, and promotes sustainable tourism practices. The development of the system in website format provides advantages in terms of accessibility, ease of content updates, rich multimedia integration, and optimization for search engines. This research makes a significant contribution to the development of technology-based tourism in areas with tourism potential that has not been maximally exposed.
SISTEM DETEKSI OTOMATIS JAMUR KULIT PADA PUNGGUNG MANUSIA MENGGUNAKAN SUPPORT VECTOR MACHINE Helena Dorothea Mbura; Yampi R Kaesmetan
Jurnal Publikasi Manajemen Informatika Vol. 4 No. 2 (2025): JURNAL PUBLIKASI MANAJEMEN INFORMATIKA
Publisher : Lembaga Pengembangan Kinerja Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/jupumi.v4i2.3839

Abstract

Skin fungus (dermatomycosis) is an infection caused by various types of fungi that develop in the epidermal layer of human skin. This infection is often difficult to detect early, especially if it occurs in hard-to-reach areas such as the back. Therefore, this study aims to develop an automatic detection system for skin fungus on the human back using the Support Vector Machine (SVM) method in digital image processing. This system is designed to help medical personnel and the general public in early detection of skin fungal infections more quickly and accurately. The methods used include image feature extraction using the Canny Edge Detection technique and classification using SVM. With the website-based system, users can upload photos of their skin to be analyzed automatically without the need for a direct visit to a health facility. The results show that this approach has a high accuracy rate in identifying skin fungal infections. Thus, this research is expected to contribute to improving the effectiveness of skin fungal disease detection and treatment in the community.
SISTEM PENDUKUNG KEPUTUSAN PEMILIHAN PUPUK PADI MENGGUNAKAN METODE PROMETHEE Stefanus Taek, Adelvino; Kaesmetan, Yampi R
Jurnal Publikasi Manajemen Informatika Vol. 4 No. 2 (2025): JURNAL PUBLIKASI MANAJEMEN INFORMATIKA
Publisher : Lembaga Pengembangan Kinerja Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/jupumi.v4i2.3879

Abstract

Padi merupakan salah satu tanaman budidaya di bidang pertanian, dan hasil dari pertanian di Indonesia yang sangat dibutuhkan sebagai bahan pokok. Faktor terpenting dalam pembudidayaan tanaman padi yaitu pupuk sebagai sumber keberhasilan dari penanaman tanaman padi. Penelitian ini bertujuan untuk menerapkap metode PROMETHEE dalam sistem pendukung keputusan pemilihan pupuk padi. Metode yang digunakan dalam penelitian ini metode PROMETHEE. PROMETHEE (Preference Ranking Organization Method for Enrichment Evaluations) merupakan suatu metode untuk menyelesaikan suatu kasus pengambilan keputusan yang menggunakan fungsi preferensi untuk membandingkan pasangan alternatif berdasarkan kriteria tertentu. Hasil yang dibangun yaitu sebuah sistem pendukung keputusan pemilihan pupuk padi. Sistem ini dibangun untuk membantu petani dalam memilih pupuk padi terbaik dengan menggunakan metode PROMETHEE secara cepat dan akurat. Dari hasil perhitungan, Pupuk tiara menjadi pilihan terbaik berdasarkan metode PROMETHEE, dengan nilai net flow tertinggi sebesar 1.26667.
PENERAPAN METODE FUZZY MAMDANI UNTUK PEMETAAN STUNTING DI KABUPATEN ENDE Andreas Curtis Hopper Fua; Yampi R Kaesmetan
Jurnal Publikasi Manajemen Informatika Vol. 4 No. 3 (2025): JURNAL PUBLIKASI MANAJEMEN INFORMATIKA
Publisher : Lembaga Pengembangan Kinerja Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/jupumi.v4i3.4172

Abstract

Stunting is a chronic growth disorder in children that impacts health, economic productivity, and quality of life. Ende Regency, located in East Nusa Tenggara Province, has recorded a high prevalence of stunting, with cases increasing by 8.2% in 2024. This research involves collecting secondary data and applying the Fuzzy Mamdani method through the stages of fuzzification, fuzzy rule formation, fuzzy inference, and defuzzification. The mapping results are expected to provide a comprehensive overview of stunting distribution and its determinants, serving as a basis for policymakers to design targeted interventions.This study also contributes to the advancement of science and technology (IPTEKS) through the application of the Fuzzy Mamdani method in health mapping and aims to raise public awareness to support stunting prevention efforts. The research aims to apply the Fuzzy Mamdani method in mapping stunting in Ende Regency, identify areas with the highest stunting prevalence, and determine the main factors influencing its distribution, enabling communities to increase awareness and participation in stunting prevention efforts based on accurate information.
Penentuan Titik Lokasi Daerah Rawan Banjir Di Kabupaten Malaka Menggunakan Metode K-Means Clustering Fransiskus Xaverius Moruk; Vito Daniel Boboy; Wilhelmina Johana Tahuk; Yota Putra Kamirsa; Yampi R Kaesmetan
Simpatik: Jurnal Sistem Informasi dan Informatika Vol. 3 No. 2 (2023): Desember 2023
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/simpatik.v3i2.2948

Abstract

Banjir di Kabupaten Malaka telah menjadi permasalahan utama dalam kehidupan sosial masyarakat. Pada musim hujan dalam waktu singkat beberapa lokasi mengalami banjir, yang mengakibatkan terhambatnya transportasi, aktivitas serta tidak jarang disertai dengan permasalahan kesehatan. Salah satu upaya untuk membantu lebih mempermudah informasi mengenai zona daerah rawan banjir dengan membuat pemetaan zona daerah rawan banjir menggunakan SIG, software QGIS dengan metode K-Means Clustering. Maka dilakukan digitasi peta kabupaten malaka agar memperoleh hasil pemetaan daerah rawan banjir. Proses pemetaan daerah rawan banjir diambil berdasarkan pengolahan data curah hujan, jenis tanah, lereng dan daerah aliran sungai Kabupaten Malaka. Hasil penelitian di peroleh 5 kelas daerah rawan banjir diantaranya sangat rawan, rawan, terancam, aman dan paling aman dengan perhitungan K-Means Clustering dengan 3 tingkat Cluster. Daerah yang berpotensi sangat rawan banjir adalah daerah yang berada di Kecamatan Malaka  Barat dan wewiku dan sedangkan daerah yang rawan banjir berada di Kecamatan Malaka Tengah dan Weliman. Kesimpulan penelitian tingginya potensi banjir dibeberapa daerah di Kabupaten Malaka selain disebabkan beberapa daerah memiliki curah hujan yang berbeda, juga dipengaruhi oleh jenis tanah, lereng dan aliran sungai.
Co-Authors Abraham Do Hina Abubakar, Muhammad A. Alfayet, Teofano E.D Andreas Curtis Hopper Fua Andrew Delfistian Dethan Anindya, Fazha Safha Atfandianus Ewal Azahra Imran, Fatimah Azis, Mayang Fitrylia Babis, Arjen Yohanes Bajuri, Miftahul K Bambali, Achmad Bahrudin Bastian Jumilton Lenggu Beda, Helena Bendi, Muhammad Indra Boboy, Vito Daniel Boling, Angel Agustina Delfince Toleu Desty A. Bekuliu Dinda Ayusma Tonael Djawas, Julaica F. Dominggus Mangngi Edwin Ariesto Umbu Malahina Elisabeth Kolastriwan Romanda Endang Oekolos Fahik, Ferdinandus Febianus Asa Frans, Harry Wolter Fransiskus Xaverius Moruk Fryonanda, Harfebi Fua, Andreas Curtis Hopper Fuzy Yustika Manik, Fuzy Yustika Ginting, Rudolf F.A. Handul, Yohanes Janssen Helena dorothea Mbura Henakin, Yohanes Bala Jamung, Maria Susanti Jekonia Nelchika Titing Jusrianto A Johannis Kamirsa, Yota Putra Katihara, Gustaf Karel Kehi, Balthasar Kembo, Emanuel Kristiano Kolihar, Reflon Paskah Komba, Clarisa La Beu, Dian Nurcahyani Ladopurab, Yohana Uba Lae, Archangela Cornelia Laoe, Desly sabatini Latuan, Franklyn Priscian Leosae, Sepriono Linus Evrianus Ama Kean Maria Claris Salzano Nurak Maria Yohana Gabriela Sasi Marlinda Vasty Overbeek Marlinda Vasty Overbeek Martin Ch. Liufeto Matulessy, Junus Yosia Eran Saktriawan Melania Zemil Meliana O Meo Mone, Bintang Vieshe Mone, Gerry Moruk, Fransiskus Xaverius Mutty, Nanda Gracenda Christina Nawa, Yesaya Laga Ndun, Alfrend Nelci Non Nesi, Maria Yunita Nimrot Doke Para Nindy Aulia Safirah Nono, Mariana Selvia Owa, Frederikus Mantolda Dede Penlaana, Vania Serafin Pua geno, Muhamad Nazhif Zuhri Putra Prawira Yohanes Puka Rafael, Simpati Gamalio Rasti Lani Rexion Alondeo Boimau Reynaldo Behar Rihi, Ivana Rosid, Achmat Saban, Aryandi Sanrina Natalia Evelin Tolan Saputri, Nur Azizah Indah Sayyid Ahmad Wisak selan, frederikus Selan, Frederikus Wanforsan Reynaldy Stefanus Taek, Adelvino Sten Dofanky Mooy Tahuk, Wilhelmina Johana Tefa, Sepri Vito Daniel Boboy Vito Daniel Boboy Vladimir Juino Jago Uko, Christianus Wilhelmina Johana Tahuk Wole, Jernianti Susanti Wulansari Masan Yafet Balan Yesaya Laga Nawa Yoman Berchmans Yota Putra Kamirsa Yunita Luruk Ulu Yustina Bete Dos Santos Yusuf Elpontus Tanaem