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All Journal Jurnal Ilmu Ternak Veteriner Indonesian Journal of Artificial Intelligence and Data Mining
Achmad Saleh
Unknown Affiliation
Computer Science & IT Veterinary

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Dairy farming production factors in Garut and Bogor Muljadi, Agus; Saleh, Achmad
Indonesian Journal of Animal and Veterinary Sciences Vol 1, No 1 (1995)
Publisher : Indonesian Animal Sciences Society

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (474.232 KB) | DOI: 10.14334/jitv.v1i1.5

Abstract

Dairy farming in West Java is still dominated by small scale. The research focussed on production factors of dairy fanning which are influencing the farmers income from selling milk . The research was carried out in 1993 via survey to 30 respondents in Garut and Bogor. The results showed that the profit earned per month from dairy farming was Rp 130,331 and Rp 118,449 in Garut and Bogor, respectively . Return to labor from dairy farming was Rp. 4.56 in Bogor and Rp. 4.38 in Garut. The production factors positively affecting the income of the farmers from selling milk were cost for barn, concentrate feed, animal health care and artificial insemination, labor, and number of lactating cows . In addition, several production factors such as cost for forages, retribution cost, and number of male calf were proven negatively affecting the income of the Canners from selling milk . Therefore, thrive related production factors should be considered in developing small scale dairy farming, not only from availability of inputs but also from institutional aspect . Key words : Production factor, milk, dairy cows
Dairy farming production factors in Garut and Bogor Agus Muljadi; Achmad Saleh
Jurnal Ilmu Ternak dan Veteriner Vol 1, No 1 (1995)
Publisher : Indonesian Center for Animal Research and Development (ICARD)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (474.232 KB) | DOI: 10.14334/jitv.v1i1.5

Abstract

Dairy farming in West Java is still dominated by small scale. The research focussed on production factors of dairy fanning which are influencing the farmers income from selling milk . The research was carried out in 1993 via survey to 30 respondents in Garut and Bogor. The results showed that the profit earned per month from dairy farming was Rp 130,331 and Rp 118,449 in Garut and Bogor, respectively . Return to labor from dairy farming was Rp. 4.56 in Bogor and Rp. 4.38 in Garut. The production factors positively affecting the income of the farmers from selling milk were cost for barn, concentrate feed, animal health care and artificial insemination, labor, and number of lactating cows . In addition, several production factors such as cost for forages, retribution cost, and number of male calf were proven negatively affecting the income of the Canners from selling milk . Therefore, thrive related production factors should be considered in developing small scale dairy farming, not only from availability of inputs but also from institutional aspect .
Early Detection of Dengue Hemorrhagic Fever Using Patient Medical Data with Ensemble Learning Methods Saleh, Achmad; Mukhtar, Ridha; Rusdah, Rusdah
Indonesian Journal of Artificial Intelligence and Data Mining Vol 8, No 3 (2025): November 2025
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/ijaidm.v8i3.38088

Abstract

Dengue Hemorrhagic Fever (DHF) remains a major public health concern in Indonesia and worldwide, where delayed diagnosis increases the risk of severe complications and mortality. Conventional laboratory-based diagnostics are time-consuming and often less accessible in resource-limited healthcare settings. This study aims to develop an early detection model for DHF using only initial clinical symptoms and demographic data extracted from electronic medical records at RSUD Brigjend H. Hasan Basry Kandangan. A total of 649 patient records (352 DHF cases and 297 non-dengue) were analyzed using the CRISP-DM framework. Five ensemble learning algorithms Random Forest, Bagging, AdaBoost, and Gradient Boosted Tree were evaluated across 80:20, 70:30, and 60:40 data splits and validated using 5-fold and 10-fold cross-validation. Random Forest consistently delivered the best and most stable performance, achieving up to 90.00 % accuracy and 0.967 AUC in the 80:20 split and mean accuracies of 88.91 % (5-fold) and 88.29 % (10-fold) in cross-validation. Further hyperparameter tuning enhanced model stability and prevented overfitting. The findings confirm that initial clinical symptoms and demographic attributes can reliably identify DHF cases early, enabling faster and more affordable screening prior to laboratory confirmation. This machine learning based decision-support model has the potential to significantly improve early clinical management of dengue fever.
Co-Authors Agus Muljadi Mukhtar, Ridha Rusdah Rusdah