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All Journal Jurnal Dinamis Jurnal Sistim Informasi dan Teknologi Journal of Soft Computing Exploration
Moh. Rahmat Irjii Matdoan
Universitas Sains dan Teknologi Jayapura
Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Industrial & Manufacturing Engineering Social Sciences

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PENYELIDIKAN POTENSI REMBESAN MINYAK BUMI DAERAH MUNUK DISTRIK SARMI SELATAN KABUPATEN SARMI PROVINSI PAPUA Lukman Nurdiansyah Reliubun; Moh. Rahmat Irjii Matdoan; Deyong P. Hindom
DINAMIS Vol 1 No 12 (2018): DINAMIS
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat Universitas Sains dan Teknologi Jayapura

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Abstract

Secara administrasi daerah penelitian berada di daerah Munuk, Distrik Sarmi Selatan Kabupaten Sarmi Provinsi Papua. Secara geografis daerah penelitian terletak pada koordinat 1380 38’ 08,2’’ – 1380 41’ 54,6” Bujur Timur (BT) dan 020 06’ 55.00” - 020 03’ 07,2” Lintang Selatan (LS). Daerah penelitian memiliki luasan 49 Km2 dengan ketinggian 125- 362.5 mdpl. Geomorfologi daerah penelitian berdasarkan relief dan genetik terdiri dari 2 (dua) satuan bentuk lahan yaitu Satuan Bentuk lahan Denudasional (D2) Munuk serta Satuan Bentuklahan Perbukitan kart (K2) Simate. Sungai yang mengalir pada daerah penelitian memiliki pola pengaliran Sub Dendritik dengan tipe genetik sungai konsekwen, isekwen dan resekwen Stadia sungai daerah penelitian adalah stadia muda menjelang dewasa. Berdasarkan singkapan batuan yang dijumpai di lapangan, maka stratigrafi daerah ini dikelompokan berdasarkan keseragaman ciri fisik, komposisi, dominasi serta hubungan antar litologi menjadi tiga satuan batuan yang secara berurutan dari tua ke muda adalah satuan batulempung, satuan batugamping, satuan batupasir. Rembesan minyak diketahui di daerah Munuk, pada daerah ini dijumpai satu titik rembesan minyak. Daerah penelitian termasuk dalam Formasi Unk, Kelompok Mamberamo (Qtu). Daerah penelitian berada dalam Zona Mamberamo yang terbentuk akibat lipatan dan sesar anjak. Munculnya rembesan minyak memberikan tanda bahwa terdapat system petroleum yang masih aktif di daerah ini. Penelitian geologi dilakukan guna mengetahui penyebab munculnya rembesan minyak serta kaitannya terhadap migrasi hidrokarbon.
PENYELIDIKAN POTENSI REMBESAN MINYAK BUMI DAERAH MUNUK DISTRIK SARMI SELATAN KABUPATEN SARMI PROVINSI PAPUA Lukman Nurdiansyah Reliubun; Moh. Rahmat Irjii Matdoan; Deyong P. Hindom
DINAMIS Vol 16 No 2 (2019): Dinamis
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat Universitas Sains dan Teknologi Jayapura

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Abstract

Secara administrasi daerah penelitian berada di daerah Munuk, Distrik Sarmi Selatan Kabupaten Sarmi Provinsi Papua. Secara geografis daerah penelitian terletak pada koordinat 1380 38’ 08,2’’ – 1380 41’ 54,6” Bujur Timur (BT) dan 020 06’ 55.00” - 020 03’ 07,2” Lintang Selatan (LS). Daerah penelitian memiliki luasan 49 Km2 dengan ketinggian 125- 362.5 mdpl. Geomorfologi daerah penelitian berdasarkan relief dan genetik terdiri dari 2 (dua) satuan bentuk lahan yaitu Satuan Bentuk lahan Denudasional (D2) Munuk serta Satuan Bentuklahan Perbukitan kart (K2) Simate. Sungai yang mengalir pada daerah penelitian memiliki pola pengaliran Sub Dendritik dengan tipe genetik sungai konsekwen, isekwen dan resekwen Stadia sungai daerah penelitian adalah stadia muda menjelang dewasa. Berdasarkan singkapan batuan yang dijumpai di lapangan, maka stratigrafi daerah ini dikelompokan berdasarkan keseragaman ciri fisik, komposisi, dominasi serta hubungan antar litologi menjadi tiga satuan batuan yang secara berurutan dari tua ke muda adalah satuan batulempung, satuan batugamping, satuan batupasir. Rembesan minyak diketahui di daerah Munuk, pada daerah ini dijumpai satu titik rembesan minyak. Daerah penelitian termasuk dalam Formasi Unk, Kelompok Mamberamo (Qtu). Daerah penelitian berada dalam Zona Mamberamo yang terbentuk akibat lipatan dan sesar anjak. Munculnya rembesan minyak memberikan tanda bahwa terdapat system petroleum yang masih aktif di daerah ini. Penelitian geologi dilakukan guna mengetahui penyebab munculnya rembesan minyak serta kaitannya terhadap migrasi hidrokarbon.
Metode Bayesian dan Multilayer Percepton dalam Mengklasifikasi Diabetes Mellitus Rasna; Moh. Rahmat Irjii Matdoan
Jurnal Sistim Informasi dan Teknologi 2022, Vol. 4, No. 2
Publisher : Rektorat Universitas Putra Indonesia YPTK Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (251.403 KB) | DOI: 10.37034/jsisfotek.v4i2.132

Abstract

Diabetes mellitus is a chronic metabolic disorder that causes glucose regulation in the blood. Blood sugar anomalies can be defined as unwanted readings either due to normal causes or reasons unknown to the patient. Machine learning applications have been widely introduced in diabetes research and blood sugar anomaly detection. However, modeling options and strategies for classification in diabetes mellitus are needed. This study aims to classify the data as diabetic or non-diabetic and improve classification accuracy. Classification accuracy is improved by using many of the data sets as training data and test data. Classification accuracy is improved by using multiple of the data set as data. In the test, the C4.5 and RF hybrid methods, as well as the MLP and Net Bayes hybrid classification methods were developed for the classification of diabetes. In the case of C4.5 + RF it provides an accuracy of 79.31% which is higher than the individual models. Similarly, MLP + Net Bayes, provides an 81.89% higher accuracy than the individual models. In the second case the 85-15% ensemble model training and test partitions have an important role for the classification of diabetes data. The proposed MLP + Net Bayes provides 81.89% accuracy as a robust model for data classification. So that the proposed model achieves the highest accuracy of 81.89% with 6 features and reaches the highest sensitivity of 64.10% and the highest specificity of 90.90%.
Hybrid support vector machine and random forest for environmental issue sentiment analysis on social media x Yulius Palumpun; Moh. Rahmat Irjii Matdoan
Journal of Soft Computing Exploration Vol. 7 No. 2 (2026): June 2026
Publisher : SHM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joscex.v7i2.127

Abstract

This study investigates the effectiveness of a hybrid machine learning model combining Support Vector Machine (SVM) and Random Forest (RF) for sentiment analysis of environmental issues discussed on social media X. A quantitative experimental research design was employed using textual data related to environmental topics collected through an application programming interface (API)-based data extraction process. Prior to model development, the collected data underwent a series of preprocessing procedures, including text normalization, tokenization, stopword elimination, and stemming. The processed text was then converted into numerical feature vectors using the Term Frequency–Inverse Document Frequency (TF-IDF) technique. To assess classification performance, three models were implemented and compared: Support Vector Machine, Random Forest, and a hybrid SVM–RF ensemble model. Model evaluation was conducted through cross-validation using accuracy, precision, recall, and F1-score as performance indicators. The experimental results revealed that the hybrid model achieved the best overall performance, attaining an accuracy of 89.10%, compared with 85.30% for Random Forest and 82.45% for Support Vector Machine. In addition, the hybrid approach generated higher precision, recall, and F1-score values, demonstrating greater robustness and consistency in sentiment classification. These findings suggest that integrating multiple machine learning algorithms can significantly enhance the analysis of complex and unstructured social media data concerning environmental issues.  
Co-Authors Deyong P. Hindom Lukman Nurdiansyah Reliubun Rasna Yulius Palumpun