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All Journal Jutisi: Jurnal Ilmiah Teknik Informatika dan Sistem Informasi Komatika: Jurnal Pengabdian Kepada Masyarakat Journal of Innovative and Creativity Indonesian Journal of Innovation Science and Knowledge JIKTEKS : Jurnal Ilmu Komputer dan Teknologi Informasi Teknik: Jurnal Teknik Mesin, Elektro dan Ilmu Komputer
Dominikus Boli Watomakin
Institut Keguruan dan Teknologi Larantuka
Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Economics, Econometrics & Finance Education Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Social Sciences Other

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Rancang Bangun Sistem Pengunjung Perpustakaan Daerah Larantuka Berbasis Web Menggunakan Metode User Centered Design Nikolaus Mario; Maria Beliti Hewen; Dominikus Boli Watomakin
Indonesian Journal of Innovation Science and Knowledge Vol. 3 No. 4 (2026): IJISK 2026
Publisher : Fakultas Pendidikan Ilmu Keguruan, Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/ijisk.v3i4.515

Abstract

Gedung baru perpustakaan di Larantuka, membuktikan bahwa pemerintah daerah sangat peduli terhadap perpustakaan untuk mengikuti perkembangan teknologi informasi sehingga dapat membantu masyarakat dalam melakukan riset, mengakses informasi dan pengetahuan. Penelitian ini bertujuan untuk merancang dan membangun sistem informasi pengunjung Perpustakaan dan Kearsipan Kabupaten Flores Timur. Permasalahan yang dihadapi adalah proses pencatatan pengunjung masih dilakukan secara manual menggunakan buku tamu. Sehingga sering terjadi kesalahan penulisan, data tidak lengkap, kesulitan dalam pencarian data, serta proses rekapitulasi yang memakan waktu lama. Metode yang digunakan dalam penelitian ini adalah User Centered Design (UCD) yang berfokus pada kebutuhan pengguna melalui tahapan analisis kebutuhan, perancangan, dan evaluasi sistem. Sistem yang dibangun menggunakan bahasa pemograman PHP, HTML, CSS, JavaScript, serta database MySQL. Hasil penelitian menunjukkan bahwa sistem yang dikembangkan mampu mempermudah proses pencatatan, pengelolaan, dan pelaporan data pengunjung secara efektif, efisien. Selain itu, sistem juga dilengkapi dengan fitur laporan otomatis, grafik pengunjung, serta export data dalam format PDF dan Excel. Berdasarkan hasil pengujian usability, sistem ini dinilai mudah digunakan dan dapat menigkatkan kinerja petugas dalam pengelolaan data pengunjung dengan Aspek efektivitas memperoleh 62,25%, aspek efisiensi memperoleh 63,86%, dan aspek kepuasan pengguna memperoleh 64,25%. Ketiga aspek berada pada kategori baik. Secara keseluruhan, aplikasi memperoleh persentase usability sebesar 63,43% dan termasuk kategori baik. Dengan demikian sistem pengunjung perpustkaan daerah Larantuka dinilai positif dan dapat membantu staf layanan tamu dalam perekapan data pengunjung.
Optimalisasi Parameter K Dalam Algoritma K-Nearest Neighbor Untuk Klasifikasi Kematangan Buah Pinang Berdasarkan Fitur Tekstur Dan Warna Sesilia Barek Tukan; Alfian Nara Weking; Dominikus Boli Watomakin
Jurnal Teknik Mesin, Elektro dan Ilmu Komputer Vol. 5 No. 2 (2025): Juli : Jurnal Teknik Mesin, Elektro dan Ilmu Komputer
Publisher : Lembaga Pengembangan Kinerja Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/teknik.v5i2.7405

Abstract

Manual determination of betel nut ripeness often takes a long time and depends on the subjectivity of the observer, so the results are inconsistent. This study aims to improve the accuracy of betel nut ripeness classification by optimizing the K parameter in the K-Nearest Neighbor (KNN) algorithm. The research process begins with the collection of 200 betel nut images that go through a preprocessing stage in the form of resizing, segmentation, and normalization. Furthermore, color feature extraction is carried out from the RGB and HSV models and texture features using the Gray-Level Co-occurrence Matrix (GLCM) method. The dataset is divided into training data and test data, then the K value is tested between 1 and 20 using the cross-validation technique. The test results show that the K value = 3 provides the highest accuracy of 85% with fairly balanced predictions in the raw, ripe, and old categories. These findings prove that selecting the appropriate K value can improve classification performance, while opening up opportunities for the application of an automated system to help farmers and industry players in determining the ripeness of betel nuts more quickly, accurately, and consistently.
Penerapan Information Gain Untuk Seleksi Fitur Pada Algoritma Naïve Bayes Untuk Analisis Sentimen Identitas Kependudukan Digital Yuliana Nogo Welan; Alfian Nara Weking; Dominikus Boli Watomakin
Jurnal Teknik Mesin, Elektro dan Ilmu Komputer Vol. 5 No. 2 (2025): Juli : Jurnal Teknik Mesin, Elektro dan Ilmu Komputer
Publisher : Lembaga Pengembangan Kinerja Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/teknik.v5i2.7433

Abstract

The rise of digital technology has driven the Indonesian government to implement Digital Population Identity (IKD) as a solution to enhance public services. However, user reviews on Google Play Store show diverse responses, requiring sentiment analysis to understand public perception. This study aims to improve sentiment classification accuracy on IKD app reviews using the Naïve Bayes algorithm optimized with Information Gain feature selection. The dataset consists of 1,000 Indonesian-language reviews manually labeled and preprocessed using text cleaning and TF-IDF feature representation. To address class imbalance, the SMOTE technique was applied. Experiments were conducted by comparing models without feature selection and balancing against those using Information Gain and SMOTE. Results indicate that the combination of Information Gain and SMOTE significantly enhances model performance, achieving 68,5% accuracy and 53,0% positive F1-Score. These findings confirm that Information Gain is effective in improving sentiment classification efficiency and accuracy. This study provides valuable insights for developing strategies to improve digital service quality. Kata kunci: Analisis Sentimen, Information Gain, Naïve Bayes, Seleksi Fitur, SMOTE.  
Diagnosis Gangguan Tidur Berdasarkan Gaya Hidup Menggunakan Algoritma Naïve Bayes Magdalena Herlin Wungubelen; Alfian Nara Weking; Dominikus Boli Watomakin
Jurnal Teknik Mesin, Elektro dan Ilmu Komputer Vol. 5 No. 2 (2025): Juli : Jurnal Teknik Mesin, Elektro dan Ilmu Komputer
Publisher : Lembaga Pengembangan Kinerja Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/teknik.v5i2.7609

Abstract

Abstract. Sleep disorders are health problems that often arise due to unhealthy lifestyle patterns and are often overlooked for their impact. This study aims to help detect the risk of sleep disorders using the Naive Bayes algorithm. Data were collected through interviews and examinations, then processed with preprocessing and testing data and achieved a classification accuracy of 88.6% for three categories: Normal, Insomnia, and Sleep Apnea. These results support the application of the Naive Bayes algorithm as a supportive diagnostic method based on lifestyle factors. This finding is also expected to serve as a basis for providing lifestyle improvement recommendations to prevent the risk pf sleep disorders.
Implementasi Random Forest Untuk Identifikasi Jenis Sampah Organik Dan Non-Organik Helminda Yeni Da Silva; Alfian Nara Weking; Dominikus Boli Watomakin
Jurnal Teknik Mesin, Elektro dan Ilmu Komputer Vol. 5 No. 2 (2025): Juli : Jurnal Teknik Mesin, Elektro dan Ilmu Komputer
Publisher : Lembaga Pengembangan Kinerja Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/teknik.v5i2.7612

Abstract

Abstract. Waste is a growing environmental problem, especially if it is not managed properly starting from the sorting process. One effort to improve the effectiveness of waste management is through automatic identification of waste types. This study aims to implement the Random Forest algorithm in the process of classifying waste into two categories: organic and non-organic waste. The data used are waste images that have gone through the preprocessing stage and the extraction of color and texture features. The Random Forest model was chosen because it has advantages in handling diverse data and providing stable classification results. Test results show that this model is capable of classifying with a fairly good level of accuracy, with the highest accuracy of 87% on the test data. In addition, this model is also integrated into a mobile application to facilitate users in identifying waste types in real-time. This implementation is expected to help the community sort waste more efficiently and contribute to sustainable environmental management.
Sistem Pakar Diagnosis Penyakit Virus Pada Ternak Babi Menggunakan Metode Fuzzy Tsukamoto Berbasis Website Elias Kapitan Bono Tefa; Alfian Nara Weking; Dominikus Boli Watomakin
Jurnal Teknik Mesin, Elektro dan Ilmu Komputer Vol. 5 No. 2 (2025): Juli : Jurnal Teknik Mesin, Elektro dan Ilmu Komputer
Publisher : Lembaga Pengembangan Kinerja Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/teknik.v5i2.7631

Abstract

Abstract. Pig farming plays a significant role in enhancing economic value, particularly in pork production. However, pigs are highly susceptible to viral infections such as Hog Cholera or Classical Swine Fever (CSF), Swine Pox, and African Swine Fever (ASF), which have high transmission and mortality rates. The limited availability of veterinary personnel and the difficulty of early diagnosis pose serious challenges for farmers. This study aims to develop a web-based expert system utilizing the Fuzzy Tsukamoto method to diagnose viral infections in pigs based on clinical symptoms. The system is designed to assist farmers in accurately identifying the type of virus and providing appropriate preventive solutions. The test results show that the Fuzzy Tsukamoto method can deliver accurate and field-relevant diagnoses. Therefore, it can be concluded that the Fuzzy Tsukamoto method is effective in developing expert systems for diagnosing viral diseases in pigs.
Co-Authors Alexander Reinaldo R.N Tobi Alfian Nara Weking Aprilia Piho Kein Bernadete Deta Elias Kapitan Bono Tefa Helminda Yeni Da Silva Lusia Bince Kumanireng Magdalena Herlin Wungubelen Maria Beliti Hewen Maria D. Elsoin Maria Katarina Liwu Martinus Irwanto Ishak Melky Falenchyl Nikolaus Mario Roberta Uron Hurit Sesilia Barek Tukan Yohanes Larantukan Yosep Kun Nogo Ama Hajon Yuliana Gunu Piran Yuliana Nogo Welan Zakarias Kuda Kumaniren