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All Journal Dinamik (JELIKU) Jurnal Elektronik Ilmu Komputer Udayana Jurnal Sarjana Teknik Informatika Indonesian Journal of Artificial Intelligence and Data Mining JurTI (JURNAL TEKNOLOGI INFORMASI) Jurnal ULTIMATICS METHODIKA: Jurnal Teknik Informatika dan Sistem Informasi Multitek Indonesia : Jurnal Ilmiah Jurnal Teknologi Terpadu Journal of Information System, Applied, Management, Accounting and Research Jurnal Sistem Informasi dan Informatika (SIMIKA) Jurnal Ilmu Komputer dan Sistem Informasi (JIKOMSI) Journal of Business and Audit Information System (JBASE) Jurnal Sosial dan Teknologi Jurnal Manajemen Informatika Jayakarta HOAQ (High Education of Organization Archive Quality) : Jurnal Teknologi Informasi Jurnal Vokasi Informatika (JAVIT) Journal Software, Hardware and Information Technology Journal of Technology and Informatics (JoTI) Simpatik: Jurnal sistem Informasi dan Informatika Indonesian Community Journal Blend Sains Jurnal Teknik Populer: Jurnal Penelitian Mahasiswa Jurnal Manajemen Informatika & Teknologi Jurnal Manajemen Informatika dan Bisnis Digital Jurnal Informatika dan Komputer (JIK) Jurnal Sistem Informasi dan Ilmu Komputer Jurnal Komputer Antartika Indonesian Journal of Education And Computer Science Saber: Jurnal Teknik Informatika, Sains dan Ilmu Komunikasi Journal Innovations Computer Science Repeater: Publikasi Teknik Informatika dan Jaringan Mars: Jurnal Teknik Mesin, Industri, Elektro dan Ilmu Komputer KETIK : Jurnal Informatika Sistematis Jurnal Sistem Informasi dan Aplikasi Nusantara Journal of Artificial Intelligence and Information Systems
Kaesmetan, Yampi R
STIKOM Uyelindo Kupang
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SISTEM PENDUKUNG KEPUTUSAN UNTUK MENENTUKAN KUALITAS DEPOT AIR MINERAL ISI ULANG MENGGUNAKAN METODE TOPSIS (TECHNIQUE FOR ORDER PREFERENCE BY SIMILARITY TO IDEAL SOLUTION) Febianus Asa; Elisabeth Kolastriwan Romanda; Jekonia Nelchika Titing; Maria Claris Salzano Nurak; Pua geno, Muhamad Nazhif Zuhri; Yampi R. Kaesmetan
METHODIKA: Jurnal Teknik Informatika dan Sistem Informasi Vol. 10 No. 1 (2024): Volume 10 Nomor 1
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46880/mtk.v10i1.2439

Abstract

The Decision Support System (DSS) for determining the quality of mineral water depots using the TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) method is an application designed to assist mineral water depot managers in selecting the best mineral water supplier based on certain criteria. The TOPSIS method is used to solve multi-criteria problems by considering the relative proximity to ideal solutions and anti-ideal solutions.First, relevant criteria for assessing the quality of mineral water are selected, including physical, chemical and microbiological parameters. Then, mineral water quality data from various suppliers is processed and normalized. Next, the normalized decision matrix is used to calculate the ideal solution and anti-ideal solution matrices. After that, a relative closeness score for each supplier is calculated based on the Euclidean distance to the ideal and anti-ideal solutions.The results of the TOPSIS analysis are used to provide recommendations for the best mineral water suppliers. By using this system, mineral water depot managers can optimize supplier selection based on predetermined quality criteria, thereby increasing customer satisfaction and maintaining the reputation of the mineral water depot in the market.
Optimisasi Stok Obat BPJS Pada Bulan April 2023 di Klinik Dompet Dhuafa Kupang Melalui Metode Topsis: Indonesia Komba, Clarisa; Bajuri, Miftahul K; Beda, Helena; Kaesmetan, Yampi R
Jurnal Sosial Teknologi Vol. 3 No. 12 (2023): Jurnal Sosial dan Teknologi
Publisher : CV. Green Publisher Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59188/jurnalsostech.v3i12.1108

Abstract

Kesehatan masyarakat menjadi aspek krusial dalam pembangunan suatu negara, dengan perhatian serius dari pemerintah dan lembaga kesehatan. Ketersediaan stok obat dan efisiensi keanggotaan BPJS Kesehatan menjadi elemen utama dalam menjamin pelayanan kesehatan yang optimal. Dalam konteks ini, implementasi Sistem Pendukung Keputusan (SPK) berbasis metode TOPSIS menjadi langkah strategis untuk meningkatkan efisiensi manajemen kesehatan. Penelitian ini difokuskan pada evaluasi dan perbaikan proses distribusi dan manajemen keanggotaan, dengan tujuan meningkatkan stok obat BPJS dan keanggotaan di Klinik Dompet Dhuafa Kupang. Pendekatan ini bertujuan memberikan pemahaman dasar tentang optimisasi stok obat dan keanggotaan BPJS, mengidentifikasi potensi masalah, dan menunjukkan kemajuan terbaru dalam bidang ini. Keberhasilan penelitian ini akan memberikan dampak positif pada kualitas layanan kesehatan di Klinik Dompet Dhuafa Kupang, memperkuat urgensi dan relevansi penelitian ini dalam konteks peningkatan pelayanan kesehatan masyarakat
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.
Penerapan K-NN (K-Nearest Neighbors) Pada Sistem Pakar Diagnosa Gejala Stunting Pada Balita Menggunakan Naïve Bayes Classifier Azis, Mayang Fitrylia; Kaesmetan, Yampi R
Sistematis Vol. 1 No. 1 (2024): Oktober 2024
Publisher : CV.RIZANIA MEDIA PRATAMA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.69533/6nxxyw62

Abstract

Kurangnya asupan gizi pada 1000 Hari Pertama Kehidupan (HPK) anak dapat memberikan dampak serius pada perkembangan fisik dan kognitif. Stunting adalah gangguan pertumbuhan fisik pada anak di bawah usia lima tahun yang ditandai dengan penurunan kecepatan pertumbuhan akibat ketidakseimbangan gizi. Presentasi angka stunting di NTT pada tahun 2023 mencapai 15,7% atau sekitar 67.538 anak, dengan Kabupaten Kupang mencatat 16,2% atau sekitar 4.899 anak mengalami stunting. Faktor ekonomi, pola asuh, riwayat infeksi, dan pengetahuan orang tua menjadi kontributor utama terhadap kondisi ini. Berdasarkan permasalahan tersebut, penerapan sistem pakar berbasis web dengan K-Nearest Neighbors (K-NN) sebagai pra-proses dan Naive Bayes Classifier sebagai klasifikasi akhir merupakan solusi potensial untuk diagnosis stunting pada balita. K-NN digunakan untuk mengelompokkan data gejala berdasarkan kemiripan karakteristik, mengidentifikasi pola terkait stunting. Kemudian, Naive Bayes Classifier menentukan diagnosis akhir melalui analisis probabilistik dari gejala yang telah diidentifikasi. Dari hasil perancangan, pengujian sistem serta evaluasi pengujian metode K-NN dan Naïve Bayes Classifier, sistem menunjukkan performa yang cukup baik dengan tingkat sensitivitas 88% dan spesifisitas 100%. Nilai True Positive sebanyak 15, True Negative sebanyak 3, False Positive sebanyak 2, dan False Negative sebanyak 0, maka diperoleh akurasi dengan confusion matrix sebesar 90% dari 20 data kasus stunting. Performa tersebut menunjukkan bahwa kedua metode ini sangat cocok untuk sistem diagnosa gejala stunting, karena terbukti mampu menyediakan diagnosa yang cepat dan akurat dalam mendeteksi dan memberikan solusi untuk menurunkan prevalensi stunting di Kabupaten Kupang.
Penentuan Titik Lokasi Daerah Rawan Banjir Di Kabupaten Malaka Menggunakan Metode K-Means Clustering Moruk, Fransiskus Xaverius; Boboy, Vito Daniel; Tahuk, Wilhelmina Johana; Kamirsa, Yota Putra; Kaesmetan, Yampi R
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.
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.
Co-Authors Abraham Do Hina Abubakar, Muhammad A. Alfayet, Teofano E.D Andrew Delfistian Dethan Anindya, Fazha Safha Atfandianus Ewal Azahra Imran, Fatimah Azis, Mayang Fitrylia Babis, Arjen Yohanes Bajuri, Miftahul K 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 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 naikteas, maria rosalinda Nawa, Yesaya Laga Ndun, Alfrend Nelci Non nenometa, elike adielwin 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 Ristiana Betris Tosi Rosid, Achmat Saban, Aryandi Sanrina Natalia Evelin Tolan Saputri, Nur Azizah Indah Sayyid Ahmad Wisak Selan, Frederikus Wanforsan Reynaldy Sten Dofanky Mooy Tahuk, Wilhelmina Johana Tefa, Sepri Vito Daniel Boboy Vladimir Juino Jago Uko, Christianus Wole, Jernianti Susanti Wulansari Masan Yafet Balan Yesaya Laga Nawa Yoman Berchmans Yunita Luruk Ulu Yustina Bete Dos Santos Yusuf Elpontus Tanaem