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All Journal Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Jurnal Online Mahasiswa (JOM) Bidang Keguruan dan Ilmu Pendidikan Chemistry in Education
Nina Fitriani, Nina
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Chemical Engineering, Chemistry & Bioengineering Chemistry Computer Science & IT Education Electrical & Electronics Engineering

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PENGARUH MODEL PROBLEM BASED LEARNING TERHADAP HASIL BELAJAR KIMIA HIDROLISIS DAN KETERAMPILAN GENERIK SAINS Fitriani, Nina; Supardi, Kasmadi Imam; Sudarmin, Sudarmin
Chemistry in Education Vol 6 No 1 (2017): Terbit Bulan April 2017
Publisher : Chemistry in Education

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Abstract

Penelitian ini bertujuan untuk mengetahui ada tidaknya pengaruh model problem basedlearning terhadap hasil belajar kimia hidrolisis dan keterampilan generik sains siswa.Desain penelitian yang digunakan adalah pretest-posttest group design. Teknik sampling yang digunakan yaitu cluster random sampling, diperoleh kelas XI MIPA 3 sebagai kelas eksperimen dan kelas XI MIPA 1 sebagai kelas kontrol. Hasil penelitian menunjukkan adanya pengaruh model problem based learning terhadap hasil belajar kimia hidrolisis dan keterampilan generik sains siswa. Hasil analisis keterampilan generik sains setelah diuji dengan N-Gain menunjukkan bahwa kelas eksperimen memperoleh 0,71 dengan kriteria tinggi dan kelas kontrol 0,61 dengan kategori sedang. Besarnya pengaruh model problem based learning terhadap hasil belajar kimia hidrolisis dan keterampilan generik sains siswa masing-masing 19,88% dan 43,2%. Berdasarkan hasil penelitian dapat disimpulkan bahwa model problem based learning berpengaruh terhadap hasil belajar kimia hidrolisis dan keterampilan generik sains siswa.
THE EFFECT OF USING PQ4R STRATEGY ON THE ABILITY OF THE SECOND YEAR STUDENTS OF MTS DARUL HIKMAH PEKANBARU IN COMPREHENDING RECOUNT TEXTS Fitriani, Nina; Supriusman, Supriusman; Safriyanti, Maria
Jurnal Online Mahasiswa (JOM) Bidang Keguruan dan Ilmu Pendidikan Vol 7, No 2 (2020): EDISI 2 JULI-DESEMBER 2020
Publisher : Jurnal Online Mahasiswa (JOM) Bidang Keguruan dan Ilmu Pendidikan

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Absract: This research aimed to find out if there is a significant effect of using PQ4R strategy on the ability of the second year students of MTs Darul Hikmah Pekanbaru in comprehending recount texts. This research took place in MTs Darul Hikmah Pekanbaru, which was collected from February to March 2020. The try out class was VIIITQA2 class (19 students) and the sample was VIIIA3 (32 students) chosen by cluster random sampling. This is a pre-experimental research with one group pretest and posttest design. This research used quantitative data and the instrument used to collect the data was a reading test in multiple choice forms. As the result, the mean score of pre-test is 59.03 and post-test is 62.97. In the other words, the mean score of post-test is higher than the mean score of pre-test. The result also showed that the value of t-test (8.907) is higher than t-table (3.365) at the significance level 0.1%. It means that Alternative Hypothesis (Ha) is accepted. It can be concluded that there is a significant effect of using PQ4R strategy on the ability of the second year students of MTs Darul Hikmah Pekanbaru in comprehending recount texts. It is suggested that in understanding recount text is one of alternatives, the teacher needs to focus on the students’ understanding of the sentences on the texts.Key Words: Effect, Reading Comprehension, PQ4R Strategy
PENGARUH MODEL PROBLEM BASED LEARNING TERHADAP HASIL BELAJAR KIMIA HIDROLISIS DAN KETERAMPILAN GENERIK SAINS Fitriani, Nina; Supardi, Kasmadi Imam; Sudarmin, Sudarmin
Chemistry in Education Vol 6 No 1 (2017): Terbit bulan April 2017
Publisher : Universitas Negeri Semarang

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Abstract

Penelitian ini bertujuan untuk mengetahui ada tidaknya pengaruh model problem basedlearning terhadap hasil belajar kimia hidrolisis dan keterampilan generik sains siswa.Desain penelitian yang digunakan adalah pretest-posttest group design. Teknik sampling yang digunakan yaitu cluster random sampling, diperoleh kelas XI MIPA 3 sebagai kelas eksperimen dan kelas XI MIPA 1 sebagai kelas kontrol. Hasil penelitian menunjukkan adanya pengaruh model problem based learning terhadap hasil belajar kimia hidrolisis dan keterampilan generik sains siswa. Hasil analisis keterampilan generik sains setelah diuji dengan N-Gain menunjukkan bahwa kelas eksperimen memperoleh 0,71 dengan kriteria tinggi dan kelas kontrol 0,61 dengan kategori sedang. Besarnya pengaruh model problem based learning terhadap hasil belajar kimia hidrolisis dan keterampilan generik sains siswa masing-masing 19,88% dan 43,2%. Berdasarkan hasil penelitian dapat disimpulkan bahwa model problem based learning berpengaruh terhadap hasil belajar kimia hidrolisis dan keterampilan generik sains siswa.
Deep Learning Model Implementation Using Convolutional Neural Network Algorithm for Default P2P Lending Prediction Nikmah, Tiara Lailatul; Jumanto, Jumanto; Prasetiyo, Budi; Fitriani, Nina; Muslim, Much Aziz
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 9 No. 3 (2023): September
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v9i3.26366

Abstract

Peer-to-peer (P2P) lending is one of the innovations in the field of fintech that offers microloan services through online channels without intermediaries. P2P  lending facilitates the lending and borrowing process between borrowers and lenders, but on the other hand, there is a threat that can harm lenders, namely default.  Defaults on  P2P  lending platforms result in significant losses for lenders and pose a threat to the overall efficiency of the peer-to-peer lending system. So it is essential to have an understanding of such risk management methods. However, designing feature extractors with very complicated information about borrowers and loan products takes a lot of work. In this study, we present a deep convolutional neural network (CNN) architecture for predicting default in P2P lending, with the goal of extracting features automatically and improving performance. CNN is a deep learning technique for classifying complex information that automatically extracts discriminative features from input data using convolutional operations. The dataset used is the Lending Club dataset from P2P lending platforms in America containing 9,578 data. The results of the model performance evaluation got an accuracy of 85.43%. This study shows reasonably decent results in predicting p2p lending based on CNN. This research is expected to contribute to the development of new methods of deep learning that are more complex and effective in predicting risks on P2P lending platforms.
Co-Authors Budi Prasetiyo, Budi Jumanto Jumanto, Jumanto Kasmadi Imam Supardi Much Aziz Muslim Nikmah, Tiara Lailatul Safriyanti, Maria Sudarmin Sudarmin Supriusman Supriusman, Supriusman