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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 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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Implementasi Metode SAW (Simple Additive Weighting) Dalam Sistem Pendukung Keputusan Seleksi Penerima Beasiswa KIP Menggunakan Algoritma Profile Matching Frans, Harry Wolter; Kaesmetan, Yampi R
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: 10.51903/1ev5md93

Abstract

Pemberian beasiswa merupakan salah satu upaya strategis untuk meningkatkan akses pendidikan dan mendorong pemerataan kesempatan belajar bagi mahasiswa berprestasi maupun yang berasal dari keluarga kurang mampu. Salah satu program beasiswa yang mendukung hal ini adalah Kartu Indonesia Pintar (KIP) Kuliah. Namun, proses seleksi penerima beasiswa seringkali menghadapi tantangan dalam hal objektivitas dan efisiensi penilaian. Penelitian ini bertujuan untuk mengembangkan sistem pendukung keputusan (SPK) dalam seleksi penerima beasiswa KIP di STIKOM Uyelindo Kupang dengan mengimplementasikan metode Simple Additive Weighting (SAW) dan algoritma Profile Matching. Metode SAW digunakan untuk menghitung nilai akhir berdasarkan pembobotan tiap kriteria, sedangkan metode Profile Matching berfungsi untuk mencocokkan profil ideal dengan data kandidat berdasarkan selisih nilai (GAP) dari setiap kriteria. Penggabungan kedua metode ini diharapkan mampu menghasilkan sistem seleksi yang lebih akurat, transparan, dan efisien dalam menentukan mahasiswa yang paling layak menerima beasiswa. Hasil akhir dari penelitian ini adalah prototipe sistem berbasis komputer yang dapat dijadikan sebagai alat bantu pengambilan keputusan oleh pihak kampus dalam proses seleksi beasiswa KIP.
Identifikasi Citra Warna Pada Kain Tenun Lotis Timor Tengah Selatan(TTS) Menggunakan Metode Convolution Network(CNN): Penerapan Convolutional Neural Networks (CNN) untuk Identifikasi Warna dalam Kain Tenun Lotis Timor Tengah Selatan: Analisis Digital pada Warisan Budaya nenometa, elike adielwin; naikteas, maria rosalinda; Kaesmetan, Yampi R
Jurnal Sistem Informasi dan Aplikasi (JSIA) Vol 2 No 1 (2024): Jurnal Sistem Informasi dan Aplikasi
Publisher : Fakultas Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52958/jsia.v2i1.7692

Abstract

Handwoven fabric is a rich cultural heritage reflecting the artistic and historical values, showcasing the cultural richness of a region. Among the diverse traditional fabrics in Indonesia, Lotis woven fabric from South Central Timor (TTS) possesses unique patterns, motifs, and colors. The identification of color images on Lotis woven fabric TTS is essential for preserving its authenticity and beauty. In the era of technological advancement, the utilization of artificial intelligence methods, particularly Convolutional Neural Networks (CNN), has garnered attention in various image processing applications. CNN has proven highly effective in classifying and recognizing patterns in images, including color identification. This study aims to implement the Convolutional Neural Networks (CNN) method in the process of identifying color images on Lotis woven fabric from South Central Timor (TTS). Through this approach, it is hoped that a system capable of recognizing and distinguishing various color combinations present in Lotis woven fabric TTS with high accuracy can be developed. This research is expected to contribute to the field of pattern recognition and image processing, as well as facilitate the preservation and development of traditional woven fabric culture, particularly Lotis woven fabric TTS.
Classification of Public Sentiment Related to Stunting in Indonesia Using BERT and SVM Kaesmetan, Yampi R
JBASE - Journal of Business and Audit Information Systems Vol 8, No 2 (2025): JBASE - Journal of Business and Audit Information Systems
Publisher : Universitas Bunda Mulia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30813/jbase.v8i2.8960

Abstract

Stunting is a serious health issue in Indonesia, including in East Nusa Tenggara Province (NTT), which requires an analysis of public perception to support data-driven policies. This study proposes sentiment analysis using deep learning and machine learning approaches to classify public opinions regarding stunting from social media/online platforms. It aims to evaluate the performance of the BERT (Bidirectional Encoder Representations from Transformers) and SVM (Support Vector Machine) models in identifying sentiment (positive, negative, neutral), compare the advantages of BERT (transformer-based) and SVM (traditional machine learning) for sentiment classification tasks, and analyze the linguistic and contextual factors influencing sentiment polarity through text feature extraction. The research methods include collecting text data from digital platforms, text preprocessing, and model training with BERT embeddings as input features for SVM. The results are compared with traditional baselines (TF-IDF and word2vec) to measure accuracy improvement. The evaluation results show that for Negative Sentiment (86 tweets) Precision: 58%, Recall: 58%, F1-Score: 58%, Accuracy: 100%. Neutral Sentiment (814 tweets) Precision: 30%, Recall: 20%, F1-Score: 25%, Accuracy: 100%. Positive Sentiment (100 tweets) Precision: 60%, Recall: 75%, F1-Score: 68%, Accuracy: 100%. Meanwhile, SVM with various kernel types showed performance differences in sentiment classification.
SISTEM PENDUKUNG KEPUTUSAN UNTUK PENERIMAAN MURID BARU PADA SMKN 4 KOTA KUPANG MENGGUNAKAM METODE PROMETHEE Mutty, Nanda Gracenda Christina; Wole, Jernianti Susanti; Ndun, Alfrend; Kaesmetan, Yampi R
MULTITEK INDONESIA Vol 17 No 2 (2023): Desember
Publisher : Universitas Muhammadiyah Ponorogo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24269/mtkind.v17i2.8152

Abstract

Kegiatan penerimaan murid baru merupakan kegiatan yang dilakukan disetiap sekolah. SMKN 4 Kota Kupang merupakan salah satu sekolah yang setiap tahunnya menyelenggarakan pendaftaran murid baru. Pendaftaran yang dilakukan masih secara manual seperti menggunakan spredsheet atau pengolah angka menimbulkan permasalahan antara lain lamanya proses pendaftaran. Penelitian ini bertujuan untuk mengembangkan sistem pendukung keputusan penerimaan murid baru menggunakan metode promethee. Di era teknologi dan komunikasi yang semakin maju, sekolah – sekolah membutuhkan sistem yang efisien dan akurat dalam memilih murid baru. Sistem pendukung keputusan sebagai salah satu alat pengambilan keputusan, kini sudah mulai banyak diterapkan dalam berbagai bidang kehidupan , tak terkecuali dalam proses pengambilan keputusan penerimaaan peserta didik baru di SMKN4 Kota Kupang. Dengan adanya sistem pendukung keputusan (SPK) Dapat membantu sekolah dalam pemilihan jurusan pada murid baru. Metode yang digunakan dalam membangun sistem pendukung keputusan, metode promethee (Preference Rangking Organization Method For Enrichment Evaluation ) adalah suatu penentuan urutan atau prioritas dalam analisis multikriteria. 
Sistem Pendukung Keputusan Penentuan Penerimaan Guru Baru Di Sma 9 Kupang (Bimoku) Menggunakan Metode Weighted Product Rihi, Ivana; Laoe, Desly sabatini; Tefa, Sepri; Kaesmetan, Yampi R
Jurnal Ilmu Komputer dan Sistem Informasi (JIKOMSI) Vol. 3 No. 1 (2020): Jurnal Ilmu Komputer dan Sistem Informasi (JIKOMSI)
Publisher : Utility Project Solution

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Perkembangan teknologi di dunia global termasuk Indonesia telah memasuki era revolusi industri 4.0 yang ditandai dengan semakin pesatnya perkembangan segala perkembangan baru tersebut yang ternyata telah menimbulkan gangguan di berbagai bidang kehidupan manusia, termasuk salah satunya yang cukup berdampak pada kehidupan manusia. dampak yang besar yaitu pada sektor pendidikan. Hal inilah yang mendorong SMA Negeri 9 Bimoku Ogan Komering Ilir memperbaiki sistem rekrutmen guru untuk meningkatkan kualitas sumber daya manusia guna memaksimalkan pembelajaran dengan menggunakan metode Weighted Product (WP) dengan kriteria penilaian dan pembobotan data kriteria tersebut dalam berupa pendidikan, pengalaman kerja, umur, tes tertulis, tes kesehatan, A1 mempunyai nilai vektor s sebesar 0,9099, sehingga nilai tersebut dibagi dengan jumlah nilai vektor s dari alternatif yang ada maka diperoleh nilai sebesar 0,1460, sehingga hasilnya sebesar 0,0836, diketahui alternatif C5 yaitu junaidi merupakan pilihan alternatif terbaik berdasarkan hasil perhitungan manual dan perangkingan akhir dari sistem pendukung keputusan rekrutmen guru.
Informasi Peringatan Dini Potensi Kekeringan Meteorologis Provinsi Nusa Tenggara Timur Bendi, Muhammad Indra; Kaesmetan, Yampi R
Jurnal Ilmu Komputer dan Sistem Informasi (JIKOMSI) Vol. 7 No. 1 (2024): Jurnal Ilmu Komputer dan Sistem Informasi
Publisher : Utility Project Solution

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55338/jikomsi.v7i1.2346

Abstract

Badan Meteorologi, Klimatologi dan Geofisika (BMKG) mempunyai tugas melaksanakan pemerintahan di bidang Meteorologi, Klimatologi, Kualitas Udara dan Geofisika sesuai dengan ketentuan perundang-undangan yang berlaku. Penelitian ini membahas sistem Informasi Peringatan Dini untuk mengidentifikasi potensi kekeringan meteorologis di provinsi Nusa Tenggara Timur. Penelitian fokus pada kekeringan yang disebakan oleh kurangnya curah hujan dan implementasinya dalam masyarakat. Metode analisis dan prakiraan spasial dan model prediktif digunakan untuk mengidentifikasi area yang berpotensi terhadap kekeringan meteorologis. Hasilnya menunjukkan efektivitas prakiraan potensi dalam memberikan peringatan dini, memungkinkan respons cepat untuk mitigasi dampak kekeringan meteorologis. Integrasi teknologi informasi dengan data meteorologis mendukung upaya adaptasi dan ketahanan terhadap bencana kekeringan. Studi ini memberikan kontribusi pada pemahaman dan pencegahan kekeringan melalui pemanfaatan teknologi informasi.
PREDIKSI HASIL PANEN PADI KABUPATEN & KOTA DI PROPINSI NUSA TENGGARA TIMUR DENGAN FUZZY INFERENCE SYSTEM (FIS) Kaesmetan, Yampi R.
HOAQ (High Education of Organization Archive Quality) : Jurnal Teknologi Informasi Vol. 10 No. 1 (2019): Jurnal HOAQ - Teknologi Informasi
Publisher : STIKOM Uyelindo Kupang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52972/hoaq.vol10no1.p42-48

Abstract

Rice (Oryza sativa) is a staple food source for the people of Indonesia. Most of the rice consumed is the result of national rice productivity. Often the government has difficulty in estimating the adequacy of basic food items that can be provided by domestic agriculture. Therefore a method is needed to predict rice yields accurately and precisely. The agricultural sector in East Nusa Tenggara is not a flagship of the community's economic activities. This is due to the geographical conditions of NTT which are less supportive for business activities in the agricultural sector. Even so, the prediction of agricultural products, especially rice yields, is needed to be predicted so that a forecast can be obtained in determining rice yields in 2017. Fuzzy logic method in this case Fuzzy Inference System (FIS) is widely applied for forecasting or prediction. Fuzzy logic has a slowness in predicting crop yields for the following year based on crop yields in the previous year and information taken from the fuzzy information provided. Fuzzyinformation can be made a rule or rule as a consideration in predicting yields. By using the formula of Mean Absolute Percentage Error (MAPE) or Average Absolute Error, from the Fuzzy Mamdani model The Fuzzy Inference System (FIS) with the Mamdani model that has been built can be used to estimate the amount of rice production in the City District in NTT with the truth value reaching 97.8%. To determine the amount of rice production in 2017, the data is processed by using the help of the Matlab 2012 fuzzy toolbox software using the centroid method for defuzzification.
PERBANDINGAN EKSTRAKSI TEKSTUR CITRA UNTUK PEMILIHAN BENIH KEDELAI DENGAN METODE STATISTIK ORDE I DAN STATISTIK ORDE II Kaesmetan, Yampi R.
HOAQ (High Education of Organization Archive Quality) : Jurnal Teknologi Informasi Vol. 10 No. 2 (2019): Jurnal HOAQ - Teknologi Informasi
Publisher : STIKOM Uyelindo Kupang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52972/hoaq.vol10no2.p92-102

Abstract

The problem in determining the selection of soybean seeds for replanting, especially in East Nusa Tenggara is still an important issue. The thing that affects the quality of soybean seeds is found broken seeds, dull seeds, dirty seeds, and broken seeds due to the process of drying and shelling. Determination of soy bean quality is usually done manually by visual observation. The manual system takes a long time and produces products with inconsistent quality due to visual limitations, fatigue, and different perceptions of each observer. This research was conducted using comparison of image texture extraction with statistical methods of order I (color moment) and order II statistics (GLCM) for soy bean selection. Order I statistics (color moment) show the probability of the appearance of the value of the gray degree of pixels in an image, while the order II statistics (GLCM) show the probability of a neighborhood relationship between two pixels that form a cohesion matrix from the image data. This research is expected to help the classification process in determining soybean seeds. The k-Nearest Neighbor (k-NN) algorithm used in previous studies to classify the image objects to be examined. The results of this study were successfully conducted using k-Nearest Neighbor (k-NN) with euclidean distance and k = 1 with the results of color moment extracts getting the highest accuracy of 88% and the results of GLCM feature extraction for homogeneity characteristics of 75.5%, correlations of 78.67% , contrast is 65.75% and energy is 63.83% with an average accuracy of 70.93%.
SISTEM PAKAR DIAGNOSA PENYAKIT IKAN GURAME DENGAN MENGGUNAKAN FIS MAMDANI Nesi, Maria Yunita; Kaesmetan, Yampi R; Meo, Meliana O.
HOAQ (High Education of Organization Archive Quality) : Jurnal Teknologi Informasi Vol. 11 No. 2 (2020): Jurnal HOAQ - Teknologi Informasi
Publisher : STIKOM Uyelindo Kupang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52972/hoaq.vol11no2.p73-80

Abstract

The carp (Osphronemus Goramy) including fish that was seeded in cultivation. In addition to the price of carp that are relatively more expensive than other fish and it has been easy to carp also has a higher value compared to other freshwater fish. But in the cultivation of carp diseases is one of the serious problems encountered by the fish farmers because it could potentially cause harm. Diseases that attack the carp both are still in the larval or adult forms of which are caused by parasitic infections in the form of fungi, protozoa, worms as well as bacterial infection of Aeromonas hydrophylla, Flexybacter colomnaris, and Mycobacterium sp. The multiplicity of types of disease that can attack the carp and the difficult process of detection because of the similarity of the symptoms caused fish farmers making it difficult to determine the methods of prevention and control of the right to address the disease. Detection of disease of carp is seen on the surface of the body of the fish. Therefore, it takes expert system to detect disease carp by involving technology. One of the methods used in the expert system of fuzzy inference system Mamdani. Fuzzy inference system Mamdani reasoning used in this study because of the handling of the value and accuisition of knowledge representation experts can directly representation in the form of rules, which can be understood when placed on the machine inference. The result of this reasoning is to detect diseases of the carp while delivering the right solution to tackle the disease of carp.
Sistem Pendukung Keputusan Pemilihan Sampah Tempat Sementara di Kota Kupang menggunakan Metode Topsis Fahik, Ferdinandus; Abubakar, Muhammad A.; Alfayet, Teofano E.D; Kaesmetan, Yampi R
Journal Innovations Computer Science Vol. 2 No. 2 (2023): November 2023
Publisher : Yayasan Kawanad

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56347/jics.v2i2.145

Abstract

Temporary waste management in the city of Kupang is an urgent problem to ensure a clean and healthy environment. Selecting the right location for a temporary waste bin is an important step in efficient waste management. In this context, Decision Support Systems (DSS) can provide valuable assistance in the decision-making process. This research proposes a Decision Support System for Selection of Temporary Waste Bins using the TOPSIS Method. The TOPSIS method (Technique for Order of Preference by Similarity to Ideal Solution) is an effective method for overcoming multi-criteria decision-making problems. This system will integrate various factors such as distance to settlements, population density, accessibility, and other environmental factors to evaluate and select optimal temporary waste bins. This decision support system will provide stakeholders, including city governments, waste management authorities, and the community, with tools that can be used to make more informed decisions in determining the location of temporary waste bins. The results of the Decision Support System analysis will provide recommendations based on the level of preference for the specified criteria, enabling stakeholders to make more informed and sustainable decisions in city waste management. It is hoped that this research can help improve efficiency in temporary waste management in Kupang City and has the potential to become a model for other cities that face similar challenges in waste management.
Co-Authors 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 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 Safirah, Nindy Aulia Saputri, Nur Azizah Indah Selan, Frederikus Wanforsan Reynaldy Sten Dofanky Mooy Tahuk, Wilhelmina Johana Tefa, Sepri Vito Daniel Boboy Vladimir Juino Jago Uko, Christianus Wisak, Sayyid Ahmad Wole, Jernianti Susanti Wulansari Masan Yafet Balan Yesaya Laga Nawa Yoman Berchmans Yunita Luruk Ulu Yustina Bete Dos Santos Yusuf Elpontus Tanaem