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All Journal JURNAL SISTEM INFORMASI BISNIS AGRISE BAREKENG: Jurnal Ilmu Matematika dan Terapan Jurnal Daya Mas : Media Komunikasi dan Informasi Hasil Pengabdian dan Pemberdayaan Masyarakat JAGO TOLIS : Jurnal Agrokompleks Tolis AGRI-TEK : Jurnal Penelitian Ilmu-ilmu Eksakta Jurnal Ilmiah Kampus Mengajar Jurnal Keilmuan dan Keislaman International Conference on Education for All
Irfan Miftahul Fauzi
Bioenterpreneur Department Universitas Muhammadiyah Madiun
Religion Agriculture, Biological Sciences & Forestry Humanities Biochemistry, Genetics & Molecular Biology Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Energy Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Mechanical Engineering Physics Public Health Social Sciences Transportation Other

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Penguatan Pendidikan sebagai Upaya untuk Meningkatkan Kemampuan Jasmani bagi Anak Pekerja Migran di Sanggar Bimbingan Malaysia Niehlah, Anis Rohadatul; Jufriansah, Adi; Khusnani, Azmi; Fauzi, Irfan Miftahul; Sari, Tria Puspita
Jurnal Ilmiah Kampus Mengajar Vol. 3, No. 2, Oktober 2023
Publisher : Asosiasi Lembaga Pendidikan Tenaga Kependidikan Perguruan Tinggi Muhammadiyah Aisyiyah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56972/jikm.v3i2.127

Abstract

Penguatan pendidikan perlu memperhatikan faktor-faktor yang dapat membantu mencapai suatu kompetensi. Hal ini bersesuaian dengan peraturan Internasional yang menyatakan bahwa setiap manusia memiliki hak memperoleh pendidikan. Namun hal ini terbentur ketika dihadapkan dengan kasus untuk usia sekolah warga imigran yang sedang mencari suaka di Malaysia. Sehingga penelitian ini bertujuan untuk memberikan gambaran mengenai pelaksanaan pendidikan sebagai upaya peningkatan kemampuan jasmani anak pekerja migran di sanggar bimbingan Malaysia selama penerjunan Kuliah Kerja Nyata kemitraan internasional program Merdeka Belajar Kampus Merdeka Perguruan Tinggi Muhammadiyah Aisyiyah berlokasi di Sanggar bimbingan Pusat Pendidikan Warga Negara Indonesia Klang. Metode yang digunakan dalam penelitian ini adalah metode kualitatif. Adapun teknik pengambilan data dalam penelitian ini melalui wawancara, dokumentasi, dan observasi. Data diperoleh dari studi pendahuluan terkait kondisi pelaksanaan pembelajaran pendidikan jasmani. Analisis data kualitatif ini menggunakan analisis Miles dan Hubermen yaitu reduksi, penyajian dan verfikasi data. Pada kegiatan kuliah kerja nyata kemitraan Internasional, peneliti mendalami dan mengamati kegiatan belajar dan mengajar pendidikan jasmani. Peneliti menyimpulkan kegiatan belajar mengajar pendidikan jasmani kurang terstruktur dan sistematis serta pendidik kurang berkompeten dibidang pendidikan jasmani sehingga akan mengpengaruhi peningkatan kemampuan jasmani anak.
ANALYSIS OF WEATHER CHANGES FOR ESTIMATION OF SHALLOT CROPS FLUCTUATION USING HIDDEN MARKOV Pradana, Yan Aditya; Azka, Dea Alvionita; Aji, Alfian Chrisna; Fauzi, Irfan Miftahul
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 16 No 1 (2022): BAREKENG: Jurnal Ilmu Matematika dan Terapan
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (762.482 KB) | DOI: 10.30598/barekengvol16iss1pp331-340

Abstract

Climate change has an impact on increasing the temperature of the earth's surface or what is known as global warming. The impact of global warming will affect the pattern of precipitation, evaporation, water run-off, soil moisture and climate variations which are very volatile can threaten the success of horticultural production, especially shallots. Shallots are a strategic commodity but are strongly influenced by fluctuations in production. The development of shallots is one of them constrained by the weather/climate which affects the production of shallots. From these constraints, shallots are also a commodity that contribute significantly to inflation. Hidden Markov Models (HMM) is one of the stochastic processes when the future only depends on condition now, in markov chain all of the element observable, and the probability move to another probability. Prediction and estimation of shallot crops with rainfall input, temperature, and humidity is done with data starting in 2016 until 2020. Estimated shallot crops follows the optimum movement pattern of prediction shallot in each of each variable. The planting months that are usually carried out in the two districts are around February, May, June and September the lowest shallot crops in April or May because transition of rainy to dry season. And the highest shallot crops in October or November. The best accuracy of estimation is rainfall factor with MAPE 5,89% with high accuracy category while 5,84% in MAPE temperature and in 5,55% in humidity factor in category high.
Classification of SWOT Statements Employing BERT Pre-Trained Model Embedding Thamrin, Husni; Oktafiani, Dewi; Rasyid, Ibrahim Ihsan; Fauzi, Irfan Miftahul
Jurnal Sistem Informasi Bisnis Vol 14, No 2 (2024): Volume 14 Nomor 2 Tahun 2024
Publisher : Diponegoro University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21456/vol14iss2pp143-152

Abstract

SWOT analysis is a highly effective method for organizations to develop strategic planning and gain widespread adoption by various institutions, industries, and businesses. The importance of SWOT analysis lies in its ability to provide a comprehensive assessment of an organization's internal and external factors. Despite its advantages, there are several challenges in its implementation, such as the challenge to identify the four elements of SWOT and to put statements into their correct position as strength, weakness, opportunity, or threat. This study aims to determine the best SWOT statement classification from a combination of using BERT models as feature extraction technique and compare it with traditional method of TF-IDF. The SWOT statement is input to the model to get a vector as a sentence representation. More similar vector representations indicate the closer meaning of the sentences. The similarity is the basis for the classifier to determine whether a sentence falls into the domain S, W, O, or T. We examined two classification algorithms, namely Support Vector Machine (SVM) and Naïve Bayes Classifier (NBC). Data consists of 635 SWOT statements from study programs of a higher education institution. Five combinations of feature extraction techniques and classification algorithms were tested. The study finds that SBERT model embedding in conjunction with support vector machine classification yield the best performance with an accuracy of 0.73 and an F1-score of 0.738. It outperforms the more traditional method of feature extraction of TF-IDF and other combinations using the Naive Bayes Classifier.
Co-Authors Adi Jufriansah Aji, Alfian Chrisna Aji, Kilau Mustika Bayu Ali Imron Al Ma'ruf Anafih, Erwinda Sam Anthon Efani Azka, Dea Alvionita Azmi Khusnani Budi Setiawan Burhan Efendi Dian Ardifah Iswari Dian Artha Kusumaningtyas Febricha Sang Dwi Anggha Gian Sugiana Sugara Harun Joko Prayitno Husni Thamrin Ihwan Susila Irawati, Diwi Acita Iswari, Dian Ardhifa Kilau Mustika Bayu Aji Lenny Puspita Dewi Liana Mangifera MOH. MUDZAKKIR Nailul Fadhilah Niehlah, Anis Rohadatul Novi Febrianti Nurur Rubingah Nurwantara, Ma’ruf Pambudi Oktafiani, Dewi Pramudito, Wysnu Putri, Sherly Octaviana Putri, Sherly Oktaviana Rasyid, Ibrahim Ihsan Rina Astuti Santosa Pradana Putra Setya Negara Sherly Oktaviana Putri Sherly Oktaviana Putri Sugeng Riyanto Suprapto Suprapto Tamtama, Noviadry Nur Tria Puspita Sari Trisakti Handayani YAN ADITYA PRADANA