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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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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

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

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

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

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.

Implementation of Dijkstra and Ant Colony Algorithms for Web-based Shortest Route Search for LPG Gas Distribution Rasna Rasna; Moh. Rahmat Irjii Matdoan; Nurlaela Kumala Dewi; Afferdhy Ariffien; Seno Lamsir
International Journal of Engineering, Science and Information Technology Vol 5, No 2 (2025)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v5i2.805

National energy needs and efforts to fulfill them are currently vital issues to be discussed and resolved. One type of energy that still has various problems is fuel gas, especially LPG (Liquid Petroleum Gas). The gas scarcity in each region differs; not all regions experience gas shortages, and some areas have excess LPG gas stocks. The problem of the scarcity of 3 kilogram (Kg) LPG gas is not the first time this has happened. In recent months, people in some regions have complained about the scarcity of subsidized 3-kilogram (kg) LPG gas. This situation certainly makes it difficult for the community. Not only does the scarcity hamper community activities, but it also makes the price of gas refills more expensive than usual. With the increasing demand for LPG gas every year, the government must provide large stocks of LPG gas. But what power if the LPG gas stock is less or runs out at specific locations. Therefore, applying gas base route search is needed to overcome the shortage of gas stock at a location. This application applies two search methods, namely the Dijkstra algorithm and the ant colony algorithm, to find the fastest route to the location of the gas base in the XYZ area. In the algorithm process, Dijkstra requires distance data for each city before starting the algorithm process. The Ant Colony Algorithm does not require the distance of each city because, in an Ant Colony, the distance between towns is calculated after the ants complete their journey. From the results of the process of the two algorithms, it is known that the path produced by Dijkstra's algorithm is more consistent and precise than the Ant Colony algorithm, which gives results that are not necessarily the same for each process.

Forecasting the Inventory of Milled Dry Grain Using the Lot Sizing Method at Markom Rice Mill Afferdhy Ariffien; Seno Lamsir; Rasna Rasna; Qurrotul Aini; Moh. Rahmat Irjii Matdoan
International Journal of Engineering, Science and Information Technology Vol 5, No 2 (2025)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v5i2.817

Today's industrial activities are increasing with sophisticated technology, all applied to satisfy consumers in various services. One of the important parts of an industry is the inventory of goods. This is related to the continuity of the resulting production. In meeting the needs of consumers, a plan is needed in production so that there is no shortage of raw materials and the production process does not stop. Grain is an agricultural commodity whose demand and production levels occasionally increase. We can see that until now, our country, Indonesia, still imports rice from other countries, where rice is the main production product produced from grain. The problem can be solved with the Lot sizing method used in this study, which is used to determine the size of the order quantity. In the rice mill itself, grain storage has a limited capacity, so it cannot be directly stored with a large capacity when the price of grain is down. Applying this method as an application will make it easier to manage grain inventory at the rice mill and estimate inventory costs for the next 1 () year. The results of the lot sizing calculation (silver meal) show that for the next year, the smallest cost of procurement of dry milled grain (MDG) demand is obtained from the procurement of demand every month. In other words, procurement is more appropriate every month or period so that the costs incurred for grain inventory are smaller. The following data are the results of the silver meal lot sizing calculation: (1) 39015.4 kg, (2) 35871.2 kg, (3) 39536 kg, (4) 33894.8 kg, (5) 31402 kg, (6) 27982.5 kg, (7) 41461.9 kg, (8) 35336.7 kg, (9) 41305.6 kg, (10) 45717.5 kg, (11) 42007.9 kg, (12) 50828 kg. The cost incurred per order is Rp. 6,000,000

Weather Classification and Prediction on Imagery Using Boltzmann Machine Rasna Rasna; Moh. Rahmat Irjii Matdoan; Junaidi Salat; Fitria J; Seno Lamsir
International Journal of Engineering, Science and Information Technology Vol 5, No 2 (2025)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v5i2.806

Weather is a physical process or event that occurs in the atmosphere at a specific time and place, as well as its changes over a short period in a particular location on Earth. To produce weather forecast information, there is a series of processes that must be carried out until the weather information is conveyed accurately. The stages involved in the feature extraction process are carried out first. This process is carried out to obtain specific characteristics or features from a dataset. After the feature extraction process has been completed, the next step is to predict the weather based on the input images. To classify the weather on Earth using various algorithms, one of which is the Machine Boltzmann. The pattern recognition method used is Machine Boltzmann as an application of a simpler and more complex method. Generally, the weather prediction system using Machine Boltzmann consists of several stages, namely image acquisition, greyscale processing, segmentation/location using Sobel edge detection, classification using the Machine Boltzmann method, and finally producing output in the form of weather class results. The classification process in this research involves images of clear, cloudy, and rainy weather as inputs. The output of the system is the determination of whether the input weather image falls into the category of clear, cloudy, or rainy weather. The results of the study show that the classification of weather based on captured images has the highest accuracy for clear weather, with a percentage of 73.33%. For cloudy weather, the success rate is equal to the error rate, which is 50%, while rainy weather was not recognized at all.

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