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All Journal Jurnal Kreatif Tadulako Online Jusikom: Jurnal Sistem Informasi Ilmu Komputer
Ruben Ruben, Ruben
Mahasiswa Jurusan Teknik Mesin, Fakultas Teknik, Universitas Diponegoro
Decision Sciences, Operations Research & Management Education

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Pemanfaatan Lingkungan Sekitar sebagai Media Pembelajaran IPS Untuk Meningkatkan Hasil Belajar Siswa Kelas III SD BK Peana Ruben, Ruben; Palimbong, Antonius; Saneba, Bonifasius
Jurnal Kreatif Tadulako Online Vol 6, No 10 (2018): Jurnal Kreatif Tadulako Online
Publisher : Jurnal Kreatif Tadulako Online

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Abstract

ABSTRAKPermasalahan utama dalam penelitian ini adalah rendahnya hasil belajar siswa pada pembelajaran IPS di kelas III SD BK Peana. Tujuan yang ingin dicapai dalam penelitian ini adalah untuk meningkatkan hasil belajar siswa pada pembelajaran IPS di kelas III SD BK Peana melalui pemanfaatan media lingkungan sekitar. Jenis penelitian adalah penelitian tindakan kelas (PTK). Desain penelitian ini mengikuti model penelitian bersiklus yang mengacu pada desain penelitian tindakan kelas yang dikemukakan oleh Kemmis dan  Mc.Taggart (Arikunto.S, 2002:84) yaitu meliputi 4 tahap: (i) perencanaan (ii) pelaksanaan tindakan (iii) observasi, dan (iv) refleksi. Subyek penelitian yaitu siswa kelas V yang berjumlah 17 orang siswa, 10 orang siswa laki-laki dan 7 orang siswa perempuan. Data yang diperoleh dalam penelitian ini meliputi hasil belajar siswa, hasil observasi aktivitas guru dan siswa. Hasil penelitian menunjukan bahwa hasil observasi aktivitas guru pada siklus I diperoleh nilai rata-rata 75%, berada dalam kategori cukup, meningkat pada siklus II menjadi 91,7%, berada dalam kategori sangat baik. Hasil observasi aktivitas siswa pada siklus I, jumlah siswa yang memperoleh nilai kategori kurang sebanyak 1 orang, yang memperoleh kategori cukup sebanyak 11 orang, dan yang memperoleh kategori baik sebanyak 5 orang. Pada siklus II, jumlah siswa yang memperoleh kategori cukup sebanyak 1 orang, yang memperoleh kategori baik sebanyak 13 orang, dan yang memperoleh kategori baik sekali sebanyak 3 orang. Hasil belajar siswa pada siklus I nilai rata-rata daya serap klasikal 69,41% dan ketuntasan belajar klasikal 64,70%. Pada siklus II nilai rata-rata daya serap klasikal 78,24% dan ketuntasan belajar klasikal 94,12%. Hal ini menunjukkan pembelajaran pada siklus II telah memenuhi indikator keberhasilan penelitian dengan nilai rata-rata daya serap klasikal minimal 70% dari skor maksimal dan ketuntasan belajar klasikal memperoleh nilai minimal 80%. Kesimpulan penelitian ini bahwa hasil belajar siswa pada pembelajaran IPS di kelas III SD BK Peana dapat meningkat melalui pemanfaatan media lingkungan sekitar. Kata Kunci : Hasil Belajar, Media Lingkungan Sekitar
Comparison of Classification Algorithm in Predicting Stroke Disease Hutabarat, Fenna Kemala; Sitompul, Daniel Ryan Hamonangan; Sinurat, Stiven Hamonangan; Situmorang, Andreas; Ruben, Ruben; Ziegel, Dennis Jusuf; Indra, Evta
Jurnal Sistem Informasi dan Ilmu Komputer Vol. 6 No. 1 (2022): JURNAL SISTEM INFROMASI DAN ILMU KOMPUTER PRIMA (JUSIKOMP)
Publisher : Fakultas Teknologi dan Ilmu Komputer Universitas Prima Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34012/jurnalsisteminformasidanilmukomputer.v6i1.2714

Abstract

ABSTRAK- To prevent stroke, we need a way to predict whether someone has had a stroke through medical parameters. With the influence of technology in the medical world, stroke can be predicted using the Data Science method, which starts with Data Acquisition, Data Cleaning, Exploratory Data Analysis, Preprocessing, and the last stage is Model Building. Based on the model that has been made, it is concluded that the algorithm with the best performance, in this case, is XGBoost with a precision value of 0.9, a recall value of 0.95, an f1 value of 0.92, and a ROC-AUC value of 0.978 after receiving five folds of cross-validation. With these results, the model created can be used to make predictions in real-time. Kata kunci : Machine Learning, Logistic Regression, Random Forest, XGBoost, Stroke
COMPARISON OF CLASSIFICATION ALGORITHM IN CLASSIFYING AIRLINE PASSENGER SATISFACTION Indra, Evta; Suwanto, Jacky; Sitompul, Daniel Ryan Hamonangan; Sinurat, Stiven Hamonangan; Situmorang, Andreas; Ruben, Ruben; Ziegel, Dennis Jusuf
Jurnal Sistem Informasi dan Ilmu Komputer Vol. 6 No. 1 (2022): JURNAL SISTEM INFROMASI DAN ILMU KOMPUTER PRIMA (JUSIKOMP)
Publisher : Fakultas Teknologi dan Ilmu Komputer Universitas Prima Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34012/jurnalsisteminformasidanilmukomputer.v6i1.2848

Abstract

In order to revive the airline industry, which is being hit by the current recession, it is essential to restore passenger confidence in airlines by improving the services provided by airlines. With the influence of technology in all industrial fields, airlines can now use Machine Learning to find the essential points that can make passengers feel satisfied with airline services and classify passenger satisfaction. This study presents the making of Machine Learning models starting from Data Acquisition, Data Cleaning, Exploratory Data Analysis, Preprocessing, and Model Building. It is concluded that Random Forest is the best algorithm used in this case study, with an F1 accuracy score of 89.4, ROC-AUC score of 0.90, and a shorter modeling period than other algorithms used in this study.
Laptop Price Prediction with Machine Learning Using Regression Algorithm Siburian, Astri Dahlia; Sitompul, Daniel Ryan Hamonangan; Sinurat, Stiven Hamonangan; Situmorang, Andreas; Ruben, Ruben; Ziegel, Dennis Jusuf; Indra, Evta
Jurnal Sistem Informasi dan Ilmu Komputer Vol. 6 No. 1 (2022): JURNAL SISTEM INFROMASI DAN ILMU KOMPUTER PRIMA (JUSIKOMP)
Publisher : Fakultas Teknologi dan Ilmu Komputer Universitas Prima Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34012/jurnalsisteminformasidanilmukomputer.v6i1.2850

Abstract

Since the COVID-19 pandemic, many activities are now carried out in a Work From Home (WFH) manner. According to data from the Central Statistics Agency (BPS) of East Java, in 2021, large and medium-sized enterprises (UMB) who choose to work WFH partially are 32.37%, and overall WFH is 2.24% (BPS East Java, 2021 ). With this percentage of 32.37%, many people need a work device (in this case, a laptop) that can boost their productivity during WFH. WFH players must have laptops with specifications that match their needs to encourage productivity. To prevent buying laptops at overpriced prices, a way to predict laptop prices is needed based on the specified specifications. This study presents a Machine Learning model from data acquisition (Data Acquisition), Data Cleaning, and Feature Engineering for the Pre-Processing, Exploratory Data Analysis stages to modeling based on regression algorithms. After the model is made, the highest accuracy result is 92.77%, namely the XGBoost algorithm. With this high accuracy value, the model created can predict laptop prices with a minimum accuracy above 80%.
Co-Authors Antonius Palimbong Bonifasius Saneba Hutabarat, Fenna Kemala Indra, Evta Siburian, Astri Dahlia Sinurat, Stiven Hamonangan Sitompul, Daniel Ryan Hamonangan Situmorang, Andreas Suwanto, Jacky Ziegel, Dennis Jusuf