PENGEMBANGAN FITUR REKOGNISI KEGIATAN DENGAN METODE SCRUM

Activity recognition from independent activities can be done according to learning outcomes. The recognition process uses Google form with submission stages, checking stages, and decision-making stages. The problems at checking stage are the conversion calculation process takes a long time and difficulty in finding data. The solution to solve is develop an activity recognition features. Development is carried out using the scrum method containing stages of user story, product backlog, sprint planning, daily scrum, sprint review, and sprint retrospective. The features produced are according to needs

Algoritme Stacking Untuk Klasifikasi Penyakit Jantung Pada Dataset Imbalanced Class

Berdasarkan data Riset Kesehatan Dasar (Riskesdas) tahun 2018, angka kejadian penyakit jantung dan pembuluh darah semakin meningkat dari tahun ke tahun. Setidaknya, 15 dari 1000 orang, atau sekitar 2.784.064 individu di Indonesia menderita penyakit jantung. Data mining merupakan bidang yang dapat menjadi solusi untuk digunakan sebagai alat deteksi dini penyakit jantung. Pada penelitian yang dilakukan sebelumnya mayoritas menggunakan single classifier, hal ini akan menimbulkan sebuah permasalahan baru ketika dalam dataset penyakit terdapat ketidakseimbangan kelas. Keberadaan ketidakseimbangan tersebut dapat menyebabkan kinerja single classifier menjadi tidak maksimal. Oleh karena itu pada penelitian ini akan digunakan metode ensemble atau meta learning. Berdasarkan pengujian yang dilakukan menunjukkan bahwa algoritme stacking mampu menghasilkan kinerja dari sisi akurasi TPR, TNR, G-Mean dan AUC yang lebih baik dibandingkan single classifier lainnya. Dengan adanya peningkatan nilai tersebut diharapkan penelitian ini mampu menjadi referensi untuk pengembagan berbagai sistem yang mendukung dan memaksimalkan tingkat keberhasilan proses deteksi dini penyakit jantung menggunakan data mining

ANALISIS SUPPORT VECTOR MACHINE PADA PREDIKSI PRODUKSI KOMODITI PADI

Analysis of Support Vector Machine (SVM) implemented on prediction of production rice commodity that can help the management of rice production in Indonesia. Prediction is done with Matlab R2016A especially function of SVM Regression. The prediction results were evaluated by performance criteria such as Root Mean Squared Error (RMSE), R-Squared and Adjusted R-Squared, and also curve fitting. SVM parameters determined automatically after processing is completed. Predictions done annually, conducted from 2006 to 2015. The results of those predictions determined the value of performance to get the value of the correspondence between the predicted value and the actual value and the best prediction is illustrated by curve fitting. It also conducted comparison performance of predictions per year to determine which ones produce the best fit. Results of prediction rice commodity with SVM method showed that the best fit is prediction in 2007 with RMSE value of 1.20E+06, R-Square of 0.794 or 79.4%, Adjusted R-Square of 0788 or 78.8%, as well as curve fitting shows the level distribution predictions are optimal for the year. Keyword: SVM, regression, predictio

DIAGNOSE OF MENTAL ILLNESS USING FORWARD CHAINING AND CERTAINTY FACTOR

The prevalence of mental disorders in Indonesia is increasingly significant, as seen from the 2018 Riskesdas data. Riskesdas records mental, emotional health problems (depression and anxiety) as much as 9.8%. This shows an increase when compared to the 2013 Riskesdas data of 6%. Based on these data, it can be said that many people still suffer from mental disorders. Meanwhile, the number of medical personnel, medicines and public treatment facilities for people with mental disorders is still limited. In addition, the lack of public awareness, concern and knowledge about mental health causes a lack of public interest in consulting a psychologist, so people tend to self-diagnose. One solution for self-diagnosis is to use an expert system. This study developed an expert system using the forward chaining method and certainty factor. Based on the research conducted, the results are as follows. First, the expert-based system that has been developed can help provide the results of a diagnosis that is carried out before there are complaints and will be detected early by efforts to increase awareness of the prevention of mental illness and reduce the tendency to self-diagnose. Second, applying the forward chaining method and certainty factor to this expert system can produce an accuracy rate of 95.918%. An expert has also validated these results; in this study, the expert was a psychologist at a hospital in Yogyakarta

Klasifikasi Penyakit Diabetes Pada Imbalanced Class Dataset Menggunakan Algoritme Stacking

Diabetes is a disease that has the potential to cause death. Based on a report from the IDF (International Diabetes Federation), it was stated that in 2019 there were 463 million people in the world suffering from this disease. According to the Ministry of Health, Indonesia is a country that is included in the top 10 highest in the world by the number of people with diabetes. Machine learning models can be a solution for the early detection of diabetes based on history and available data. The majority of the research that has been done chiefly uses a single classifier. The single classifier model has a weakness when faced with class imbalance conditions in the dataset. Therefore, this study uses the Stacking Model for the classification and prediction process on the diabetes dataset. The goal is to improve the performance of a single classifier. In addition, the Stacking Model is expected to be one of the solutions for the classification of diabetes in the imbalanced class dataset. Based on two test experiments that have been carried out using two different datasets. The Stacking algorithm can produce an accuracy value of 89%, TPR value of 89%, TNR value of 85%, and G-Mean of 86.98% in the first dataset and can produce an accuracy value of 96%, TPR value of 96%, TNR value of 94%, and G-Mean of 94.99% in the second dataset. These results are better than single classifiers such as C4.5, K-NN, and SVM of the four indicators evaluated in both diabetes datasets. Thus, the proposed algorithm, namely Stacking (C4.5-SVM), can be a solution for classifying diabetes datasets with unbalanced class distribution conditions.</jats:p

Implementation of Scrum Method in the Learning Activity Monitoring Feature Outside the Study Program

Learning activities outside the study program can be participated by active students in the current semester through the credit recognition mechanism according to the study program policy. These activities are monitored by Field Supervisors (DPL) through certain communication media. The obstacles that arise such as students not contacting DPL or communicating with DPL, not reporting activities regularly, only asking for a signature on the final report, and only asking DPL to fill out an activity assessment. The activity recording information system &nbsp;that available only has features for submitting proposals, recording activities and reporting activities for study program admin users. So that it is necessary to develop a feature for monitoring learning activities outside the study program to make it easier for DPL to monitor student activities in certain semesters. The information system was created using the MySQL database and the Codeigniter 3 framework. Feature development was created using the Scrum method with a focus on determining priority features, working on priority features and reviewing the results of feature work. This can help to get information system output quickly according to development needsKegiatan pembelajaran di luar program studi dapat diikuti oleh mahasiswa aktif pada semester berjalan melalui mekanisme pengakuan sks sesuai kebijakan program studi. Kegiatan tersebut dipantau oleh Dosen Pembimbing Lapangan (DPL) melalui media komunikasi tertentu. Kendala yang muncul antara lain mahasiswa tidak menghubungi DPL atau berkomunikasi dengan DPL, tidak melaporkan kegiatan secara berkala, hanya meminta tanda tangan pada laporan akhir, dan hanya meminta DPL mengisi penilaian kegiatan. Sistem informasi pencatatan kegiatan yang tersedia hanya memiliki fitur pengajuan proposal, pencatatan kegiatan dan pelaporan kegiatan untuk pengguna admin program studi. Sehingga diperlukan pengembangan fitur pemantauan kegiatan pembelajaran di luar program studi untuk memudahkan DPL memantau kegiatan mahasiswa pada semester tertentu. Sistem informasi dibuat menggunakan database MySQL dan framework Codeigniter 3. Pengembangan fitur dibuat menggunakan metode scrum dengan fokus pada penentuan fitur prioritas, pengerjaan fitur prioritas dan review hasil pengerjaan fitur. Hal tersebut dapat membantu untuk mendapatkan output sistem informasi dengan cepat sesuai kebutuhan pengembanga

PENERAPAN METODE SCRUM PADA PENGEMBANGAN SISTEM INFORMASI PENCATATAN MAGANG

Pencatatan kegiatan magang masih menggunakan google form dan pengecekan dokumen menggunakan Microsoft Excel. Kendala pada pencatatan kegiatan magang yaitu perlu mengubah secara berkala dan data banyak sulit dikelola arsipnya. Solusi untuk mengatasi kendala dengan mengembangkan sistem informasi pencatatan dengan metode scrum. Implementasi dilakukan dengan tahapan product backlog, sprint planning, daily scrum, sprint review, dan sprint retrospective. Kebutuhan fitur beserta detail fitur sudah ditentukan oleh stakeholder. Implementasi fitur dilakukan bertahap sesuai sprint planning yang sudah ditetapkan. Evaluasi dilakukan melalui sprint review dan sprint retrospective. Hasil sprint review menunjukkan setiap hasil pengerjaan sudah sesuai dengan estimasi pengerjaan dengan penambahan dan perbaikan pada fitur pendaftaran kegiatan dan log book. Hasil sprint retrospective menunjukkan keseluruhan hasil pengerjaan sesuai product backlog dengan detail penamaan file otomatis yang perlu ditambahkan pada fitur pendaftaran kegiatan, konfirmasi kegiatan, dan download atau export. Pengembangan sistem informasi dengan metode scrum bisa menghasilkan output fitur secara cepat

IMPLEMENTASI METODE SCRUM PADA PEMBUATAN FITUR USULAN DAN KLAIM KONVERSI APLIKASI XYZ

Program pembelajaran di luar kurikulum formal bisa diikuti oleh mahasiswa pada semester tertentu. Pelaksanaankegiatan dari program tersebut dapat dikonversi ke kredit di dalam kurikulum formal, melalui pengajuan usulandan klaim konversi. Pengajuan masih dilakukan menggunakan Google Form. Pengolahan data usulan dan klaimkonversi dilakukan menggunakan Google Sheet. Kendala pengolahan data usulan dan klaim konversi antara lainsemua data ada pada satu file sehingga komponen data kadang terhapus, proses membuka file memerlukan waktulama untuk data banya, dan setiap semester perlu membuat file baru untuk pengarsipan. Proses pembuatandilakukan dengan metode scrum dengan tahapan product backlog, sprint backlog, sprint, dan sprint review.Metode scrum dapat membantu mengurutkan prioritas fitur yang dikerjakan sesuai kebutuhan dan fitur yang belumsesuai dengan kebutuhan bisa disesuaikan sebelum mengerjakan fitur berikutnya. Mahasiswa dapat mengajukanusulan dan klaim konversi sesuai jenis program yang diikuti. Admin dapat melakukan pengolahan data usulan danklaim konversi pada fitur yang tersedia