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All Journal IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Jurnal Ilmiah Flash Indonesian Journal of Data and Science International Journal of Artificial Intelligence in Medical Issues
Nicodemus Mardanus Setiohardjo
Politeknik Negeri Kupang
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Electrical & Electronics Engineering Health Professions Mathematics Medicine & Pharmacology Public Health

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PENGEMBANGAN WEBSITE UNIT PENELITIAN DAN PENGABDIAN KEPADA MASYARAKAT DAN PENERAPAN JURNAL ELEKTRONIK BERBASIS OPEN SOURCE DI POLITEKNIK NEGERI KUPANG Imanuel Christian Mauko; Nicodemus Mardanus Setiohardjo; Fredrik Paulus Noach
Jurnal Ilmiah Flash Vol 3 No 2 (2017)
Publisher : P3M- Politeknik Negeri Kupang

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (2280.398 KB) | DOI: 10.32511/flash.v3i2.145

Abstract

Salah satu kegiatan UP2M PNK sebagai pengelola kegiatan penelitian dan pengabdian kepadamasyarakat di Politeknik Negeri Kupang adalah menyampaikan informasi terkait pelaksanaankegiatan penelitian dan pengabdian masyarakat kepada civitas akademik Politeknik Negeri Kupangyang dilakukan melalui penyampaian surat kepada tiap unit dan jurusan di Politeknik Negeri Kupangataupun melalui media sosial seperti WhatsApp. Adapun kelemahan dari penyampaian informasimelalui media surat (kertas) tidak dapat menjangkau seluruh civitas akademik PNK secara luas.Begitu juga dengan menggunakan media WhatsApp yang hanya menjangkau dosen yang tergabungdalam grup WhatsApp tersebut.Saat ini di Politeknik Negeri Kupang sudah terdapat beberapa jurnal ilmiah yang diterbitkansecara berkala, yaitu jurnal Mitra, Saintec, Flash (Teknik Elektro), Juteks (Teknik Sipil), Jaka(Akuntansi), dan Bisman (Bisnis Manajemen). Dari hasil penelitian sebelumnya telah dilakukanpenerapan jurnal elektronik (e-journal) untuk jurnal Flash dan Jaka menggunakan aplikasi e-journalberbasis open source yaitu Open Journal Systems (OJS). Berdasarkan rencana kerja dari UnitPenelitian dan Pengabdian Kepada Masyarakat Politeknik Negeri Kupang (UP2M PNK) yangberkoordinasi dengan pengelola jurnal bahwa akan diterapkan jurnal elektronik pada seluruh jurnalyang ada di lingkungan Politeknik Negeri Kupang (PNK).Tujuan dari penelitian ini adalah mengembangkan sebuah website bagi UP2M PNK yang dapatmembantu UP2M PNK dalam menyampaikan informasi terkait kegiatan penelitian dan pengabdianmasyarakat di Politeknik Negeri Kupang, serta mengimplementasikan jurnal elektronik untukseluruh jurnal yang ada di Politeknik Negeri Kupang menggunakan Open Journal Systems (OJS).Tahapan-tahapan dalam melakukan penelitian ini mengacu pada metode pengembangan web yangmeliputi: (1) Analysis, (2) Design, (3) Generation, (4) Implementation
Pemetaan Dan Analisis Tingkat Radiasi Gelombang Extremely Low Frequency (Elf) Pada Permukaan Bumi Tertentu Sebagai Suatu Pendekatan Ilmiah Terhadap Fenomena Paranormal (Studi Kasus Pada Tempat-Tempat Angker Di Kabupaten Alor) Imanuel Christian Mauko; Robinson A Wadu; Nicodemus Mardanus Setiohardjo
Jurnal Ilmiah Flash Vol 6 No 2 (2020)
Publisher : P3M- Politeknik Negeri Kupang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32511/flash.v6i2.716

Abstract

The existence of Extremely Low Frequency (ELF) waves in several places on earth, also influences the socio-cultural mindset of certain people who strongly believe in paranormal, superstitious and mystical stories about the existence of ghosts around them. One of the places known for its paranormal practice is the people who inhabit Alor Island in Alor Regency, East Nusa Tenggara, Indonesia. This paranormal phenomenon still cannot be explained logically, so that sometimes it causes complicated social problems in society, even the stikma of "Orang Alor Suanggi" appears in social interactions. This research was conducted to find out the existence of Earth's Extremely Low Frequency (ELF) electromagnetic field radiation at a certain location with a case study in Alor Regency. This research was conducted by measuring the level of Extremely Low Frequency (ELF) wave radiation using an EMF detector at haunted / sacred places in Alor Regency and connecting it with paranormal events in the community. The results of the research prove that several locations in Alor Regency have high exposure to ELF wave radiation and the effect of the earth's magnetic field is quite large. The village in Subo Village which has the highest observed ELF wave radiation with value at 815 V / M, while the highest instantaneous ELF wave radiation value is the Village in Kaipera at 953 V / M and the highest magnetic field effect value is at the Peak of Timingmang Hill which is 132.52 mT. Locations where exposure to ELF radiation waves were observed at all times, including those with high supernatural power backgrounds, according to the residents on Alor Island.
Analisis Tekstur untuk Klasifikasi Motif Kain (Studi Kasus Kain Tenun Nusa Tenggara Timur) Nicodemus Mardanus Setiohardjo; Agus Harjoko
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 8, No 2 (2014): July
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.6545

Abstract

AbstrakIndonesia memiliki banyak kekayaan budaya dalam bentuk kain tradisional, salah satunya kain tenun dari Nusa Tenggara Timur (NTT). Kain tenun dari tiap etnik di NTT memiliki cirikhas motif masing-masing yang merupakan manifestasi kehidupan sehari-hari, kebudayaan dan kepercayaan masyarakat setempat. Di mata pemerhati kain tenun NTT, asal kain tenun dapat diketahui dari motifnya. Tidak semua orang dapat membedakan asal daerah dari motif kain tenun tertentu dikarenakan sulitnya mendefinisikan karakteristik motif kain tenun suatu daerah dan beragamnya motif kain tenun yang ada dan komposisi warna yang beragam pula.Analisis tekstur adalah teknik analisis citra berdasarkan anggapan bahwa citra dibentuk oleh variasi intensitas piksel, baik citra keabuan maupun warna. Motif kain tenun terbentuk dari variasi intensitas warna sehingga dapat dipandang sebagai tekstur berwarna dari kain tenun. Penelitian ini bertujuan untuk mengetahui diantara pendekatan analisis tekstur menggunakan Gray Level Co-occurrence Matrix (GLCM) yang dikombinasikan dengan momen warna dan pendekatan analisis tekstur menggunakan Color Co-occurrence Matrix (CCM), metode manakah yang memberikan hasil lebih baik untuk klasifikasi motif kain tenun NTT.Hasil penelitian menunjukkan bahwa untuk klasifikasi motif kain tenun NTT, pendekatan analisis tekstur menggunakan metode CCM memberikan hasil lebih baik dibandingkan pendekatan analisis tekstur menggunakan GLCM yang dikombinasikan dengan momen warna. Kata kunci—klasifikasi citra, GLCM, CCM, momen warna, motif kain tenun NTT AbstractIndonesia have many culture in the form of traditional fabrics, one of them is woven fabric from Nusa Tenggara Timur (NTT). Each NTT ethnic has motif characteristic which ismanifestation of daily life, culture and the faith of local people. For a NTT woven fabric observer, the origin of a woven fabric can be known from the motif. But its difficult to recognising the origin of a woven fabrics because it is hard to define the characteristics of woven fabric motif from a region and wide variety of existing woven fabric motifs and also color composition.Texture analysis is image analysis technique based on assumption that an image formed by the variation of pixels intensity, both gray and color image. Woven fabric motif formed by the variation of color intensity that can be seen as color texture of the woven fabric. This study aims to determine between texture analysis using GLCM combined with color moment and texture analysis using CCM, which method gives better results for the NTT woven fabric motif classification.The results showed that for the NTT woven fabric motif classification, texture analysis using CCM gives better results than the texture analysis using GLCM combined with color moment. Keywords— image classification, GLCM, CCM, color moment, NTT woven fabric motif
Leveraging K-Nearest Neighbors for Enhanced Fruit Classification and Quality Assessment Iwan Sudipa, I Gede; Azdy, Rezania Agramanisti; Arfiani, Ika; Setiohardjo, Nicodemus Mardanus; Sumiyatun
Indonesian Journal of Data and Science Vol. 5 No. 1 (2024): Indonesian Journal of Data and Science
Publisher : yocto brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/ijodas.v5i1.125

Abstract

This study investigates the application of the K-Nearest Neighbors (KNN) algorithm for fruit classification and quality assessment, aiming to enhance agricultural practices through machine learning. Employing a comprehensive dataset that encapsulates various fruit attributes such as size, weight, sweetness, crunchiness, juiciness, ripeness, acidity, and quality, the research leverages a 5-fold cross-validation method to ensure the reliability and generalizability of the KNN model's performance. The findings reveal that the KNN algorithm demonstrates high accuracy, precision, recall, and F1-Score across all metrics, indicating its efficacy in classifying fruits and predicting their quality accurately. These results not only validate the algorithm's potential in agricultural applications but also align with existing research on machine learning's capability to tackle complex classification problems. The study's discussions extend to the practical implications of implementing a KNN-based model in the agricultural sector, highlighting the possibility of revolutionizing quality control and inventory management processes. Moreover, the research contributes to the field by confirming the hypothesis regarding the effectiveness of KNN in agricultural settings and lays the foundation for future explorations that could integrate multiple machine learning techniques for enhanced outcomes. Recommendations for subsequent studies include expanding the dataset and exploring algorithmic synergies, aiming to further the advancements in agricultural technology and machine learning applications.
Predicting Cardiovascular Disease Using Machine Learning: A Feature Engineering and Model Comparison Approa Waluyo Poetro, Bagus Satrio; Zulfikar, Dian Hafidh; Sunia Raharja, I Made; Setiohardjo, Nicodemus Mardanus
International Journal of Artificial Intelligence in Medical Issues Vol. 3 No. 2 (2025): International Journal of Artificial Intelligence in Medical Issues
Publisher : Yocto Brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/ijaimi.v3i2.363

Abstract

Cardiovascular disease (CVD) remains one of the leading causes of mortality globally, emphasizing the need for early detection and effective risk stratification. With the increasing availability of clinical and lifestyle-related health data, machine learning (ML) has become a powerful tool to support data-driven diagnosis and decision-making in healthcare. This study aims to develop and evaluate multiple supervised ML models to predict the presence of cardiovascular disease based on non-invasive features obtained from routine medical checkups. The dataset, comprising 69,301 individual records, includes variables such as age, gender, blood pressure, cholesterol, glucose levels, body measurements, and lifestyle habits. Following comprehensive data cleaning and feature engineering such as the derivation of BMI, Mean Arterial Pressure (MAP), and Pulse Pressure four classifiers were applied: Logistic Regression, Random Forest, Gradient Boosting, and Support Vector Machine (SVM). Model performance was evaluated using metrics including accuracy, precision, recall, F1-score, and ROC-AUC. Among all models tested, the Gradient Boosting Classifier achieved the highest performance, with a ROC-AUC score of 0.8060 and a balanced precision-recall tradeoff, indicating strong discriminatory power. Visualizations such as ROC curves and confusion matrices confirmed the superior capability of Gradient Boosting in differentiating between patients with and without CVD. These findings demonstrate the viability of ML-driven risk assessment models as decision-support tools in clinical settings, potentially aiding in earlier diagnosis and more personalized intervention strategies.
Co-Authors Agus Harjoko Dian Hafidh Zulfikar Fredrik Paulus Noach I Gede Iwan Sudipa Ika Arfiani Imanuel Christian Mauko Imanuel Christian Mauko Poetro, Bagus Satrio Waluyo Rezania Agramanisti Azdy, Rezania Agramanisti Robinson A Wadu Sumiyatun Sunia Raharja, I Made