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Analisis Peran Penyuluh Pertanian dalam Pengembangan Usaha Tani Jambu Mete (Studi Kasus: Kelurahan Watulea, Kecamatan Gu, Kabupaten Buton Tengah) Yusran, Muhammad; Sahusilawane, Aphrodite M; Damanik, Inta P N
Jurnal Agrosilvopasture-Tech Vol 4 No 1 (2025): Jurnal Agrosilvopasture-Tech
Publisher : Universitas Pattimura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/j.agrosilvopasture-tech.2025.4.1.46

The objective of this research was to analyze the role of agricultural extension worker in developing cashew farming in Watulea Village, Gu District, Buton Tengah Regency based on productivity of cashew farm that still in low category, whereas this village is one of the cashew production centers in Sulawesi Tenggara Province. There were various problems faced by cashew farmers which require the presence of agricultural extension workers as mentor, facilitator and organizer. Respondents were 60 cashew farmers representing 120 cashew farmers in this area and were taken at simple random sampling using lottery numbers from a list of names of all cashew farmers. Primary data was collected through structured interview with respondents using questionnaires; while secondary data was collected from various literature sources and related agencies such as the local village head's office. Data analysis was carried out descriptively qualitatively using data tabulation and preparing intervals for each research variable based on the highest value (score) and the lowest value (score). The result showed that according to farmers' assessment, the three roles of extension worker have been carried out well, there were only a few indicators in the role of instructor that still need to be improved, namely training farmers to choose and use medicines to prevent and control pests and diseases as well as fertilizers appropriately. Also developing ideas or concepts and putting them into practice to motivate farmers to develop farming businesses. As facilitator, extension workers have been considered good by farmers, as well as as organizer.

Analisis evaluasi kinerja pendampingan Rumah BUMN Majene pada usaha mikro mahasiswa Safira, Dela; Ilyas, Herlina; Yusran, Muhammad
AKURASI: Jurnal Riset Akuntansi dan Keuangan Vol 7 No 2 (2025)
Publisher : LPMP Imperium

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36407/akurasi.v7i2.1711

This study aims to evaluate the performance of Rumah BUMN Majene mentoring for student micro-enterprises. Mentoring is considered necessary in supporting the development of entrepreneurial skills and increasing the capacity of student businesses. This study uses a descriptive qualitative approach. The results of the study indicate that the mentoring program is effective and has a positive impact, both in terms of packaging design, business legality, digital marketing, and financial management. The success of the program is supported by the active involvement of Rumah BUMN staff, collaboration with universities, and the implementation of training and coworking spaces. The evaluation was conducted using the CIPP (Context, Input, Process, Product) model, which indicates that this program has been aligned with the needs of business stakeholders. However, improvements are needed in legal assistance and financial management training so that the program can run more optimally. Public interest statements This statement affirms the commitment to supporting micro-student entrepreneurs through the Rumah BUMN Majene program. It aims to enhance skills, access, and knowledge among young entrepreneurs, thereby contributing to sustainable economic growth at both local and national levels. Through collaboration among various institutions, this program has the potential to serve as a successful model for community empowerment and economic strengthening.

Effect of Feature Normalization and Distance Metrics on K-Nearest Neighbors Performance for Diabetes Disease Classification Yusran, Muhammad; Sadik, Kusman; Soleh, Agus M; Suhaeni, Cici
Journal of Mathematics, Computations and Statistics Vol. 8 No. 2 (2025): Volume 08 Nomor 02 (Oktober 2025)
Publisher : Jurusan Matematika FMIPA UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/jmathcos.v8i2.8012

Diabetes is a global health issue with a steadily increasing prevalence each year. Early detection of the disease is an important step in preventing severe complications. The K-Nearest Neighbors (KNN) algorithm is often used in disease classification, but its performance is highly influenced by the choice of normalization method and distance metric used. This study aims to evaluate the effect of various normalization methods and distance metrics on the performance of the KNN algorithm in diabetes disease classification. The three normalization methods were employed: z-score normalization, min-max scaling, and median absolute deviation (MAD). In addition, the seven distance metrics were assessed: Euclidean, Manhattan, Chebyshev, Canberra, Hassanat, Lorentzian, and Clark. The dataset used is Pima Indians Diabetes which consists of 768 observations and 8 features. The data were split into 80% training data and 20% test data, and using 5-fold cross-validation to determine the optimal k value. The results show that the MAD-Canberra combination produces the highest overall accuracy, recall, and F1-score of 87.32%, 82.33%, and 81.94%, respectively. The highest precision was obtained from the Baseline-Hassanat combination at 86.96%, while the lowest performance was observed for the Z-Score-Chebyshev combination with F1-Score 58.02%. These results highlight that no single combination universally outperforms others, underscoring the need for empirical evaluation. Nonetheless, combining MAD normalization with metrics such as Canberra or Hassanat can serve as a strong starting point for developing KNN-based classification systems, especially in medical contexts that are sensitive to misclassification.

Analisis evaluasi kinerja pendampingan Rumah BUMN Majene pada usaha mikro mahasiswa Safira, Dela; Ilyas, Herlina; Yusran, Muhammad
AKURASI: Jurnal Riset Akuntansi dan Keuangan Vol 7 No 2 (2025)
Publisher : LPMP Imperium

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36407/akurasi.v7i2.1711

This study aims to evaluate the performance of Rumah BUMN Majene mentoring for student micro-enterprises. Mentoring is considered necessary in supporting the development of entrepreneurial skills and increasing the capacity of student businesses. This study uses a descriptive qualitative approach. The results of the study indicate that the mentoring program is effective and has a positive impact, both in terms of packaging design, business legality, digital marketing, and financial management. The success of the program is supported by the active involvement of Rumah BUMN staff, collaboration with universities, and the implementation of training and coworking spaces. The evaluation was conducted using the CIPP (Context, Input, Process, Product) model, which indicates that this program has been aligned with the needs of business stakeholders. However, improvements are needed in legal assistance and financial management training so that the program can run more optimally. Public interest statements This statement affirms the commitment to supporting micro-student entrepreneurs through the Rumah BUMN Majene program. It aims to enhance skills, access, and knowledge among young entrepreneurs, thereby contributing to sustainable economic growth at both local and national levels. Through collaboration among various institutions, this program has the potential to serve as a successful model for community empowerment and economic strengthening.

Evaluating Fasttext and Glove Embeddings for Sentiment Analysis of AI-Generated Ghibli-Style Images Sentana Putra, I Gusti Ngurah; Yusran, Muhammad; Sari, Jefita Resti; Suhaeni, Cici; Sartono, Bagus; Dito, Gerry Alfa
Journal of Applied Informatics and Computing Vol. 9 No. 5 (2025): October 2025
Publisher : Politeknik Negeri Batam

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

The development of text-to-image generation technology based on artificial intelligence has triggered mixed public reactions, especially when applied to iconic visual styles such as Studio Ghibli. This research aims to evaluate public sentiment towards the phenomenon of Ghibli-style AI images by comparing two static word embedding methods, namely FastText and GloVe, on three classification algorithms: Logistic Regression, Random Forest, and Convolutional Neural Network (CNN). Data in the form of Indonesian tweets were collected from Twitter using hashtags such as #ghibli, #ghiblistyle, and #hayaomiyazaki during the period 25 March to 25 April 2025. Each tweet was manually labelled with positive or negative sentiment, then preprocessed and represented using pre-trained FastText and GloVe embeddings. Evaluation was conducted using accuracy, precision, recall, and F1-score metrics, both macro and weighted. Results showed that FastText consistently performed the best on most models, especially in terms of precision and overall accuracy, thanks to its ability to handle sub-word information and spelling variations in social media texts. The combination of CNN with FastText yielded the highest performance with a macro F1-score of 76.56% and accuracy of 84.69%. However, GloVe still showed competitive performance in recall on the Logistic Regression model, making it relevant for contexts that prioritise sentiment detection coverage. This study emphasizes the importance of selecting embeddings and models that are appropriate to the characteristics of the data and the purpose of the analysis in informal social media-based sentiment classification.

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