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All Journal E-Dimas: Jurnal Pengabdian kepada Masyarakat BAREKENG: Jurnal Ilmu Matematika dan Terapan Prosiding Seminar Nasional Pendidikan Matematika Universitas Pattimura Jambura Journal of Biomathematics (JJBM) Tensor: Pure and Applied Mathematics Journal MATHunesa: Jurnal Ilmiah Matematika Pakem : Jurnal Pengabdian Kepada Masyarakat Pattimura International Journal of Mathematics (PIJMath) Parameter: Jurnal Matematika, Statistika dan Terapannya PENGAMATAN: Jurnal Pengabdian Masyarakat untuk Ilmu MIPA dan Terapannya
RAHAKBAUW, DORTEUS LODEWYIK
MATHEMATIC DEPARTMENT FACULTY OF MATHEMATICS AND NATURAL SCIENCES UNIVERSITY OF PATTIMURA
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Journal : Tensor: Pure and Applied Mathematics Journal

 1 
Penerapan Jaringan Saraf Tiruan Learning Vector Quantization Untuk Pemetaan Wilayah Berpenduduk Miskin di Provinsi Maluku Dorteus Lodewyik Rahakbauw; Venn Yan Ishak Ilwaru
Tensor: Pure and Applied Mathematics Journal Vol 1 No 1 (2020): Tensor : Pure And Applied Mathematics Journal
Publisher : Department of Mathematics, Faculty of Mathematics and Natural Sciences, Pattimura University, Ambon, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/tensorvol1iss1pp25-30

Abstract

Badan Pusat Statistik (BPS) stated that the number of poor people in Indonesia reached 28.01 million people based on data as of March 2016. This figure is around 10.86 percent of the national population. Province of Maluku as the third poor contributor of all provinces in Indonesia reached 27.74 percent. Note that, there are 8 of total 11 districts/cities in Maluku which are determined as underdeveloped regions (Kementerian PDT, 2015), Maluku Barat Daya (MBD) is one of them. Based on data from BPS, in 2014 the percentage of poor people in district of MBD reached 28.33 percent being the second highest district in Maluku after Maluku Tenggara Barat (MTB). It is quite difficult make the poverty level of MBD lower, due to a large number of villages in MBD have some economic access isolations because of geographical conditions. Various programs and policies in social and health have been done to solve this poverty problem, but still could not overcome this problem yet. In this paper we have grouped the districts/cities of Maluku based on poverty factors using Learning Vector Quantization (LVQ) method. The results of this research showed that there are 5 poverty clusters in Maluku. Those are: Cluster 1 consists of Maluku Tenggara Barat, Maluku Utara dan Buru; cluster 2 consists of Maluku Tengah; cluster 3 consists of Kep. Aru, Seram Bagian Barat dan Seram Bagian Timur, cluster 4 consists of Maluku Barat Daya dan Buru Selatan; and cluster 5 consists of Ambon and Tual. Each cluster describes the poverty level with respect to its Partition matrix respectively. The results that we obtained also show that cluster 4 has the highest poverty level.
Perbandingan Logika Fuzzy Metode Sugeno dan Metode Mamdani Untuk Deteksi Dini Penyakit Stroke Dorteus Lodewyik Rahakbauw; Adya Afriananda; Henry W. M. Patty
Tensor: Pure and Applied Mathematics Journal Vol 3 No 1 (2022): Tensor: Pure and Applied Mathematics Journal
Publisher : Department of Mathematics, Faculty of Mathematics and Natural Sciences, Pattimura University, Ambon, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/tensorvol3iss1pp11-22

Abstract

Stroke is a neurological function disorder caused by disruption of blood flow in the brain that arises suddenly and acutely within a few seconds or more precisely within a few hours that lasts more than 24 hours with symptoms or signs according to the affected area. Early detection of stroke usually takes a long time. With advances in technology, stroke can be prevented by detecting the risk early so that it can be treated quickly and increase the chances of recovery. The discussion of this research is about early detection of stroke risk by comparing using fuzzy logic Sugeno method and Mamdani method and using patient data at Dr. Hospital. H. Isaac Umarella. By using input variables in the form of: blood pressure, age, LDL, and blood sugar levels. Based on the results obtained from the calculation of Error with Mean Absolute Percentage Error (MAPE), the level of truth of the calculation of the Sugeno method is 87%, while the truth level of the Mamdani method is 85% so that it can be said that both methods get good results but Sugeno's fuzzy logic is superior with a value of small MAPE. In conclusion, fuzzy logic with the Sugeno method can be used in early detection of stroke risk.
Analisis Perbandingan Optimasi Stochastic Gradient Descent dan Adaptive Moment Estimation dalam Klasifikasi Emosi dari Audio Menggunakan Convolutional Neural Network Tutuhatunewa, Aldelia Jocelyn; Rahakbauw, Dorteus Lodewyik; Leleury, Zeth Arthur
Tensor: Pure and Applied Mathematics Journal Vol 6 No 1 (2025): Tensor: Pure and Applied Mathematics Journal
Publisher : Department of Mathematics, Faculty of Mathematics and Natural Sciences, Pattimura University, Ambon, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/tensorvol6iss1pp13-22

Abstract

Emotion plays a fundamental role in human life, influencing behavior, social interaction, anddecision-making. Successful communication and understanding between individuals depend greatly on ourability to recognize and express emotions. In this context, sound or audio plays a key role as a medium thatreflects and conveys human emotional expression. In the era of information technology and artificialintelligence, emotion recognition through sound has become a growing focus of research. Machine learningalgorithms, particularly neural networks, can be trained to understand and classify emotions conveyed invarious forms, including text, images, videos, and audio. Among these algorithms, Convolutional NeuralNetwork (CNN) has shown promising performance in emotion classification tasks. In this study, thecomparison between Stochastic Gradient Descent (SGD) and Adaptive Moment Estimation (Adam)optimizers in emotion classification from audio using CNN is investigated. The research aims to determinethe optimal optimizer for emotion classification tasks. The results suggest that SGD optimizer outperformsAdam in terms of overall accuracy, with SGD achieving 53% accuracy compared to Adam's 48% accuracy inThe Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS) dataset. Therefore, foremotion classification from audio data, Stochastic Gradient Descent (SGD) optimizer is recommended forbetter performance.
Co-Authors Adya Afriananda Beay, Lazarus Kalvein Ferry Kondo Lembang Frejon Tahya H. W. M. Patty Haumahu, Gabriella Ilwaru, Venn Yan Ishak Lexy Janzen Sinay, Lexy Janzen Marsa Sopaheluwakan Megawati Zein Waliulu Norisca Lewaherilla Rijoly, Monalisa E. Rukua, Abdul Wahid Rumfot, Rindyani Salmon Notje Aulele, Salmon Notje Samin Radjid Tanassy, Muh. Iskandar Tomasouw, Berni Pebo Tomasouw, Berny P. Tuhumury, Eunike J. M. Tutuhatunewa, Aldelia Jocelyn Wattimena, Abraham Zacaria Yopi Andry Lesnussa Zeth Arthur Leleury, Zeth Arthur