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All Journal METHODIKA: Jurnal Teknik Informatika dan Sistem Informasi Jurnal Pengabdian Pada Masyarakat METHABDI TAMIKA: Jurnal Tugas Akhir Manajemen Informatika & Komputerisasi Akuntansi
Humuntal Rumapea
Methodist University Of Indonesia
Religion Agriculture, Biological Sciences & Forestry Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Immunology & microbiology Languange, Linguistic, Communication & Media Medicine & Pharmacology Physics Public Health

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Penerapan Metode Support Vector Machine untuk Pengenalan Pola Aksara Batak Toba Panjaitan, Efdi Sarjono; Rumapea, Humuntal; Jaya, Indra Kelana
TAMIKA: Jurnal Tugas Akhir Manajemen Informatika & Komputerisasi Akuntansi Vol 4 No 2(SEMNASTIK) (2024): TAMIKA: Jurnal Tugas Akhir Manajemen Informatika & Komputerisasi Akunt
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46880/tamika.Vol4No2(SEMNASTIK).pp49-55

Abstract

The usage of the Batak Toba script has declined, and its complex forms pose challenges in pattern recognition. This study employs the Support Vector Machine (SVM) method to classify Batak Toba script patterns, utilizing a Histogram of Oriented Gradients (HOG) as a feature extraction technique. The data used comes from various sources, totaling 285 script images. After preprocessing, SVM was applied to separate characters into two main classes, which were further subdivided into subclasses until final classification was achieved. The results show that the combination of HOG and SVM can classify Batak Toba script characters with an accuracy of 89,47%. This research makes a significant contribution to the preservation of the Batak Toba script and has broader potential applications in pattern recognition and image classification.
PEMANFAATAN GOOGLE EARTH ENGINE DAN ALGORITMA RANDOM FOREST UNTUK PEMETAAN LAHAN PERKEBUNAN JERUK Dian Agaventa, Chrissandro; Rumapea, Humuntal; Indra Kelana Jaya
METHODIKA: Jurnal Teknik Informatika dan Sistem Informasi Vol. 11 No. 2 (2025): Volume 11 Nomor 2 Tahun 2025
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46880/mtk.v11i2.4078

Abstract

This study employed Google Earth Engine (GEE) and the Random Forest algorithm to map citrus plantations in Silimakuta District, Simalungun Regency,  North Sumatra. As a major citrus production center—reaching 840,000 quintals in 2023—the region faces challenges in producing accurate and efficient maps of plantation distribution. By processing Sentinel-2 and Sentinel-1 satellite imagery in GEE, this study provides a more detailed and reliable mapping solution. The Random Forest model achieved a land-cover classification accuracy of 97% and a Kappa coefficient of 96.3%, demonstrating the method’s effectiveness for land mapping. This approach can overcome existing limitations in land data and deliver visual information useful for increasing citrus plantation productivity in the region. Therefore, the combined use of Google Earth Engine and the Random Forest algorithm shows strong potential to support more optimal and sustainable land management.
PENYULUHAN TEKNOLOGI INFORMASI DAN KOMUNIKASI BAGI SISWA-SISWI PADA SMA GKPI PADANG BULAN MEDAN Larosa, Fati Gratianus Nafiri; Rumapea, Humuntal; Manalu, Darwis Robinson; Maslan, Jhoni; Rumapea, Sri Agustina; Sarkis, Indra M.; Hasibuan, Doli; Rumapea, Yolanda Y. P.; Rajagukguk, Edward; Gea, Asaziduhu; Silalahi, Arina Prima; Samosir, Nettina; Aritonang, Mendarissan; Lumbanraja, Posma; Purba, Mufria J.
Jurnal Pengabdian Pada Masyarakat METHABDI Vol 3 No 1 (2023): Jurnal Pengabdian Pada Masyarakat METHABDI
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46880/methabdi.Vol3No1.pp66-70

Abstract

Information technology plays a very important role in the world of education, especially when blended learning is carried out, where there is a combination of face-to-face and virtual meetings. In addition, Blended Learning is a part or element of social interaction that grows and develops among students. Face-to-face meetings prioritize direct interaction both gestures or body language, voice intonation and eye gaze. There is strong information technology support that bridges interpersonal interactions, so this also influences the interactions between teachers/educators and students. Practical, eye-catching and easy to carry around, making Laptops or Smartphones a very popular Information and Communication Technology product, Blended Learning is no exception. The applications used are also very easy to install and some are even free, for example Kahoot!, Quizizz, Padlet and Canva.
Penerapan Metode Support Vector Machine untuk Pengenalan Pola Aksara Batak Toba Panjaitan, Efdi Sarjono; Rumapea, Humuntal; Jaya, Indra Kelana
TAMIKA: Jurnal Tugas Akhir Manajemen Informatika & Komputerisasi Akuntansi Vol 4 No 2(SEMNASTIK) (2024): TAMIKA: Jurnal Tugas Akhir Manajemen Informatika & Komputerisasi Akunt
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46880/tamika.Vol4No2(SEMNASTIK).pp49-55

Abstract

The usage of the Batak Toba script has declined, and its complex forms pose challenges in pattern recognition. This study employs the Support Vector Machine (SVM) method to classify Batak Toba script patterns, utilizing a Histogram of Oriented Gradients (HOG) as a feature extraction technique. The data used comes from various sources, totaling 285 script images. After preprocessing, SVM was applied to separate characters into two main classes, which were further subdivided into subclasses until final classification was achieved. The results show that the combination of HOG and SVM can classify Batak Toba script characters with an accuracy of 89,47%. This research makes a significant contribution to the preservation of the Batak Toba script and has broader potential applications in pattern recognition and image classification.
PEMANFAATAN GOOGLE EARTH ENGINE DAN ALGORITMA RANDOM FOREST UNTUK PEMETAAN LAHAN PERKEBUNAN JERUK Dian Agaventa, Chrissandro; Rumapea, Humuntal; Indra Kelana Jaya
METHODIKA: Jurnal Teknik Informatika dan Sistem Informasi Vol. 11 No. 2 (2025): Volume 11 Nomor 2 Tahun 2025
Publisher : Universitas Methodist Indonesia

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

Abstract

This study employed Google Earth Engine (GEE) and the Random Forest algorithm to map citrus plantations in Silimakuta District, Simalungun Regency, North Sumatra. As a major citrus production center—reaching 840,000 quintals in 2023—the region faces challenges in producing accurate and efficient maps of plantation distribution. By processing Sentinel-2 and Sentinel-1 satellite imagery in GEE, this study provides a more detailed and reliable mapping solution. The Random Forest model achieved a land-cover classification accuracy of 97% and a Kappa coefficient of 96.3%, demonstrating the method’s effectiveness for land mapping. This approach can overcome existing limitations in land data and deliver visual information useful for increasing citrus plantation productivity in the region. Therefore, the combined use of Google Earth Engine and the Random Forest algorithm shows strong potential to support more optimal and sustainable land management.
Co-Authors Aritonang, Mendarissan Darwis Robinson Manalu Dian Agaventa, Chrissandro Fati Gratianus Nafiri Larosa Gea, Asaziduhu Hasibuan, Doli Hasibuan, Doli Indra Kelana Jaya Jhoni Maslan, Jhoni Lumbanraja, Posma Mufria J. Purba Panjaitan, Efdi Sarjono Rajagukguk, Edward Rajagukguk, Edward Rumapea, Yolanda Y. P. Samosir, Nettina Sarkis, Indra M. Siboro, Indrayana Silalahi, Arina Prima Sri Agustina Rumapea Yunita, Ana