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Predicting the Number of Forest and Land Fire Hotspot Occurrences Using the ARIMA and SARIMA Methods Santoso, Angga Bayu; Widodo, Tri
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol 13, No 1 (2024): MARET
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v13i1.2018

Abstract

Forests are an area and part of the environmental cycle that is very important for survival because forests are areas on Earth that regulate the balance of the ecosystem. Forest fires rank second only to illegal logging in Indonesia's list of forest destruction causes. Forest fires can occur due to two factors, namely natural and human factors. Therefore, the hotspot factor that can cause forest fires is an independent variable. The population of hotspots in the West Kalimantan region in 2020 amounted to 1,416 spots. This study aims to predict the number of hotspot occurrences on land and forests that cause fires before the fires spread and are challenging to overcome or extinguish. The method to indicate the number of hotspot occurrences uses the Autoregressive Integrated Moving Average (ARIMA) and Seasonal Autoregressive Integrated Moving Average (SARIMA) methods. Modeling ARIMA (0,1,1) and SARIMA (0,1,1) (2,2,1)12 obtained Root Mean Square Error (RMSE) evaluation results for ARIMA of 6.61 while SARIMA of 7.61. The ARIMA's Mean Squared Error (MSE) evaluation value is 43.70, and the SARIMA is 58.05. Based on these results, it can be concluded that the ARIMA model provides excellent and accurate performance in describing the trend of hotspot events that will occur in the future with a smaller RMSE value compared to SARIMA.
Pembangunan Model Prediksi Potensi Kebakaran Hutan dan Lahan Menggunakan Algoritma Machine Learning Berdasarkan Data Patroli Santoso, Angga Bayu; Sitanggang, Imas Sukaesih; Hardhienata, Medria Kusuma Dewi
JURNAL INFOTEL Vol 16 No 3 (2024): August 2024
Publisher : LPPM INSTITUT TEKNOLOGI TELKOM PURWOKERTO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/infotel.v16i3.1180

Abstract

Indonesia allocates 120 million hectares or 64% of its land area as forest areas. Indonesia's forests continue to experience deforestation; one of the causes is forest and land fires (karhutla). The government conducts forest and land fire prevention through integrated patrols with the Forest and Land Fire Prevention Patrol Information System (SIPP Karhutla) facility for patrol data management. However, the patrol data are still primarily used for data observation and simple spatial analysis in the spatial module. Patrol data has not been used for further forest and land fire prevention studies. Based on these problems, this research aims to build a prediction model of potential forest and land fires using SVM, Random Forest, and XGBoost algorithms and compare model performance to get the best model. The preprocessing stage uses the SMOTE-ENN method to handle data class imbalance, and the k-fold cross-validation stage and hyperparameter tuning use the random search method. The confusion matrix evaluation method to see the model performance in terms of accuracy is XGBoost (94.81%), Random Forest (90.23%), SVM-linear (79.58%), SVM-polynomial model (73.99%), SVM-rbf (74.26%), and SVM-sigmoid (35.04%). Therefore, the best prediction model is XGBoost (94.81%) with boosting technique. The results of this study have implications for helping early prevention of forest and land fires on the islands of Sumatra and Kalimantan.
Penerapan Penyeimbangan Data Pada Analisis Sentimen Ulasan Game Magic Chess Go Go di Play Store dengan Naive Bayes Mustaqim, Muhammad Hafizd; Santoso, Angga Bayu
Building of Informatics, Technology and Science (BITS) Vol 7 No 2 (2025): September 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i2.7845

Abstract

This study aims to perform sentiment analysis on reviews of the Magic Chess Go Go game from the Google Play Store, which exhibits data imbalance with 2,949 negative sentiment entries and 1,537 positive ones. To address this issue, a sentiment classification model was developed using the Naïve Bayes algorithm, along with a comparison of four data balancing methods: SMOTE, ADASYN, Random Oversampling (ROS), and Random Undersampling (RUS). Evaluation was conducted using a confusion matrix under two data splitting schemes, with the 80:20 split yielding the best performance. In this scheme, SMOTE achieved the highest accuracy at 84.2%, followed by ADASYN (83.8%), ROS (82.9%), and RUS (77.9%). These results indicate that SMOTE is the most effective method for handling data imbalance in this context. It can be concluded that applying SMOTE to the Naïve Bayes model in the 80:20 split scenario provides the best performance, demonstrating that synthetic data generation through SMOTE helps balance the dataset without significant information loss. Future work may explore alternative algorithms and parameter tuning to enhance sentiment classification performance.
Predicting the Number of Forest and Land Fire Hotspot Occurrences Using the ARIMA and SARIMA Methods Santoso, Angga Bayu; Widodo, Tri
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol. 13 No. 1 (2024): MARET
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v13i1.2018

Abstract

Forests are an area and part of the environmental cycle that is very important for survival because forests are areas on Earth that regulate the balance of the ecosystem. Forest fires rank second only to illegal logging in Indonesia's list of forest destruction causes. Forest fires can occur due to two factors, namely natural and human factors. Therefore, the hotspot factor that can cause forest fires is an independent variable. The population of hotspots in the West Kalimantan region in 2020 amounted to 1,416 spots. This study aims to predict the number of hotspot occurrences on land and forests that cause fires before the fires spread and are challenging to overcome or extinguish. The method to indicate the number of hotspot occurrences uses the Autoregressive Integrated Moving Average (ARIMA) and Seasonal Autoregressive Integrated Moving Average (SARIMA) methods. Modeling ARIMA (0,1,1) and SARIMA (0,1,1) (2,2,1)12 obtained Root Mean Square Error (RMSE) evaluation results for ARIMA of 6.61 while SARIMA of 7.61. The ARIMA's Mean Squared Error (MSE) evaluation value is 43.70, and the SARIMA is 58.05. Based on these results, it can be concluded that the ARIMA model provides excellent and accurate performance in describing the trend of hotspot events that will occur in the future with a smaller RMSE value compared to SARIMA.
Penerapan aplikasi SIJENTIK DBD dalam pencegahan demam berdarah dengue Perdana, Agung Aji; Nuryani, Dina Dwi; Santoso, Angga Bayu; Pratama, Muhammad Putra; Kartini, Maharani
JOURNAL of Public Health Concerns Vol. 5 No. 9 (2025): JOURNAL of Public Health Concerns
Publisher : Indonesian Public Health-Observer Information Forum (IPHORR) Kerja sama dengan: Unit Penelitian dan Pengabdian Kep Akademi Keperawatan Baitul Hikmah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56922/phc.v5i9.1584

Abstract

Background: Indonesia, as a tropical country, faces a high burden of vector-borne infectious diseases, particularly dengue fever (DHF), transmitted by the Aedes aegypti mosquito. The high number of DHF cases in Indonesia, including in South Lampung, is influenced by environmental factors, community behavior, and the limitations of manual recording systems for monitoring mosquito larvae. Vector control efforts through national strategies such as 3M Plus and mosquito nest eradication have been implemented, but their effectiveness remains hampered by data accuracy and community participation. In the digital era, the innovative mobile application-based Larvae Recording System (SIJENTIK) offers a solution to improve accuracy, speed, and community engagement in dengue prevention. A community service program in Hajimena Village, South Lampung, aims to empower residents as independent mosquito larvae monitors through the application of SIJENTIK, enabling real-time mosquito larvae monitoring and supporting more targeted health interventions. Purpose: Increase public awareness of dengue fever (DHF) and promote the use of the SIJENTIK DBD application. Method: The activity was conducted in 2025 in Hajimena Village, South Lampung, involving health cadres, health workers, and the community as respondents. The activities included education on dengue hemorrhagic fever (DHF), photo recording of invasive species, manual recording of SIJENTIK data from Aedes aegypti survey forms, and ovitrap construction. The recording was carried out using the SIJENTIK digital application, an interface application that can be installed on smartphones and used in real time. The SIJENTIK DBD application was used as a substitute for the observational technique of recording mosquito larvae through inputting data into the application dashboard, where input data would be directly processed, accumulated, and accessed in real time. Results: This demonstrated a 14.6% increase in knowledge and skills of health workers in recording mosquito larvae. Descriptive data showed that manual recording was slow, data was inaccurate, reporting time was required, and interventions were not timely. Meanwhile, with the SIJENTIK DBD application, the recording process is faster, the data is more accurate, data can be input directly digitally, reports are updated at any time, and intervention actions are faster and more targeted. Conclusion: The SIJENTIK DBD program's educational activities effectively increased the knowledge of healthcare workers, strengthened their ability to record mosquito larvae, and facilitated community monitoring and health education. This digital reporting system accelerated communication, increased transparency, and encouraged community participation, enabling SIJENTIK DBD to become an efficient community-based intervention model for dengue control. Suggestion: Expanding education and implementing SIJENTIK DBD in schools and ensuring its continued implementation at the district level is necessary, along with training for cadres and support from local government policies. Active community involvement as independent mosquito larvae monitors (jumantik) also needs to be increased to ensure consistent monitoring and more effective reduction in dengue cases. Keywords: Community empowerment; Dengue fever; Healthcare workers; SIJENTIK DBD application Pendahuluan: Indonesia sebagai negara tropis menghadapi beban tinggi penyakit menular berbasis vektor, terutama Demam Berdarah Dengue (DBD) yang ditularkan oleh nyamuk aedes aegypti. Tingginya kasus DBD di Indonesia, termasuk di Lampung Selatan, dipengaruhi oleh faktor lingkungan, perilaku masyarakat, serta keterbatasan sistem pencatatan manual dalam pemantauan jentik. Upaya pengendalian vektor melalui strategi nasional seperti 3M Plus dan pemberantasan sarang nyamuk telah dilaksanakan, namun efektivitasnya masih terkendala oleh akurasi data dan partisipasi komunitas. Di era digital, inovasi Sistem Pencatatan Jentik (SIJENTIK) berbasis aplikasi seluler hadir sebagai solusi untuk meningkatkan akurasi, kecepatan, dan keterlibatan masyarakat dalam pencegahan DBD. Program pengabdian masyarakat di Desa Hajimena, Lampung Selatan, bertujuan memberdayakan warga sebagai jumantik mandiri melalui penerapan SIJENTIK, sehingga pemantauan jentik dapat dilakukan secara realtime dan mendukung intervensi kesehatan yang lebih tepat sasaran. Tujuan: Meningkatkan pengetahuan tentang demam berdarah dengue (DBD) dan sosialisasi penerapan aplikasi SIJENTIK DBD pada masyarakat. Metode: Kegiatan dilaksanakan pada tahun 2025 di Desa Hajimena, Lampung Selatan, dengan melibatkan kader kesehatan, tenaga kesehatan, dan masyarakat sebagai responden. Kegiatan berupa penyuluhan mengenai demam berdarah dengue (DBD), pencatatan foto spesies invasif, pencatatan manual SIJENTIK dari formulir survei aedes aegypti, serta konstruksi ovitrap. Pelaksanaan pencatatan menggunakan aplikasi digital SIJENTIK yang merupakan suatu aplikasi interface, dapat diinstalasi pada smartphone dan dapat digunakan secara realtime. Penggunaan aplikasi SIJENTIK DBD adalah sebagai pengganti dalam teknik pencatatan jentik nyamuk hasil observasi yaitu dengan cara menginput pada dashboard aplikasi, dimana data input akan secara langsung diproses, di akumulasi, dan di akses secara realtime. Hasil: Menunjukkan peningkatan pengetahuan dan kemampuan pekerja kesehatan sebesar 14.6% dalam mencatat jentik nyamuk. Secara deskriptif menunjukkan bahwa dengan pencatatan manual dalam proses pelaksanaan lambat, datanya kurang akurat, diperlukan waktu tertentu untuk membuat laporan, dan tindakan intervensi tidak tepat waktu. Sedangkan, dengan aplikasi SIJENTIK DBD mendapatkan proses pencatatan lebih cepat, datanya lebih akurat, data dapat dinput langsung secara digital, update laporan setiap saat, dan tindakan intervensi menjadi lebih cepat dan tepat sasaran. Simpulan: Kegiatan edukasi program SIJENTIK DBD efektif meningkatkan pengetahuan tenaga kesehatan, memperkuat kemampuan pencatatan jentik, serta memudahkan masyarakat memantau laporan dan memperoleh edukasi kesehatan. Sistem pelaporan digital ini mempercepat komunikasi, meningkatkan transparansi, dan mendorong partisipasi komunitas, sehingga SIJENTIK DBD dapat menjadi model intervensi berbasis masyarakat yang efisien dalam penanggulangan DBD. Saran: Perlunya perluasan edukasi dan penerapan SIJENTIK DBD ke sekolah serta penerapan berkelanjutan di tingkat kabupaten, disertai pelatihan kader dan dukungan kebijakan pemerintah daerah. Keterlibatan aktif masyarakat sebagai jumantik mandiri juga perlu ditingkatkan agar sistem pemantauan berjalan konsisten dan mampu menekan angka kasus DBD secara lebih efektif.