Suhartono Suhartono

State University of Makassar

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Co-Authors

Dedy Dwi Prastyo

Sepuluh Nopember Institute of Technology

Muhammad Hisyam Lee

University of Technology Malaysia

Heri Kuswanto

Sepuluh Nopember Institute of Technology

Subanar Subanar

Universitas Gadjah Mada

Santi Puteri Rahayu

Sepuluh Nopember Institute of Technology

Tri Saptuti Susiani

Sebelas Maret University

Moh Salimi

Sebelas Maret University

Ratna Hidayah

Sebelas Maret University

Bambang Widjanarko Otok

Sepuluh Nopember Institute of Technology

Taat Kuspriyono

Universitas Informatika dan Bisnis Indonesia

Cooperative Institutions

Sepuluh Nopember Institute of Technology

179

Diponegoro University

112

Sebelas Maret University

107

National Research and Innovation Agency

Universitas Negeri Surabaya

State University of Malang

National Nuclear Energy Agency of Indonesia

State University of Makassar

State University of Semarang

Universitas Informatika dan Bisnis Indonesia

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An Analysis Of The Credit Risk Determinant Using Internal Factor And Macroeconomy Data

International Journal of Advanced Multidisciplinary (2023)

Nisa Kornelia Abdul Mongid Suhartono Suhartono

This study analyzes the influence of internal bank factors and macroeconomic factors on non-performing loans (NPL) using data from the listed banks in Indonesia. We apply panel data analysis. The bank's internal and macroeconomic variables used in this study include inflation, GDP, BOPO, CAR, bank size, LDR, and ROA. The sample includes all listed banks that cover 40 banks for the period 2016 to 2021. The analysis technique uses descriptive analysis and linear regression analysis. The results of the study show that banks with a high capital adequacy ratio (CAR) own a low credit risk (NPL). While the inefficiency (BOPO) is positive and significant confirming the bad managed bank also having bad credit risk management. GDP growth has a positive coefficient but statistically not significant. Internal variables such as Bank Size, LDR and ROA are all not significant. The result confirmed that well capitalized bank take less risk and inefficient banks have bad credit risk management.

Factor (programming language)

10.38035/ijam.v1i4.166

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Opportunities and Challenges for Sustainable Bioremediation of Natural and Synthetic Estrogens as Emerging Water Contaminants Using Bacteria, Fungi, and Algae

Water Air & Soil Pollution (2021)

Anisa Ratnasari Achmad Syafiuddin Ahmad Beng Hong Kueh Suhartono Suhartono Tony Hadibarata

10.1007/s11270-021-05183-3

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Citations (19)

Hybrid forecasting model to predict air passenger and cargo in Indonesia

2018 International Conference on Information and Communications Technology (ICOIACT) (2018)

Ratna Sulistyowati Suhartono Suhartono Heri Kuswanto Setiawan Erni Tri Astuti

Forecasting of air passenger and cargo have a major influence on the master plan of the airport infrastructure development and investment by the civil airline. This research aims to obtain the most accurate predictive value of the air passenger and cargo at three international airports Indonesia, namely, Soekarno Hatta, I Gusti Ngurah Rai, and Juanda Airport. Those international airports are the three largest contributors to the number of air passengers and cargo volumes in Indonesia. This research uses a hybrid forecasting method that combines linear and nonlinear models. The combination of two linear and nonlinear models is able to obtain accurate predictions. The first phase is linear modeling with time series regression model (TSR) and Autoregressive Integrated Moving Average with Exogenous Factor (ARIMAX). In the second phase, the error of the linear model is analyzed by using machine learning methods such as Neural Network (NN) and Support Vector Regression (SVR) to capture nonlinear patterns. There are four hybrid models that be applied and compared, i.e. TSR-NN, TSR-SVR, ARIMAX-NN, and ARIMAX-SVR based on the Mean Absolute Percentage Error (MAPE). The results show that hybrid ARIMAX-NN and TSR-NN give more accurate prediction than hybrid TSR-SVR and ARIMAX-SVR.

10.1109/icoiact.2018.8350816

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Citations (19)

PENYUSUNAN ALAT EVALUASI ONLINE MENGGUNAKAN QUIZZIZ TERINTEGRASI DENGAN GOOGLE CLASSROOM

J-ABDI Jurnal Pengabdian kepada Masyarakat (2021)

Suhartono Suhartono Herfa Maulina Dewi Soewardini

Quizziz adalah salah satu diantara banyak aplikasi yang dapat digunakan guru dalam proses pembelajaran, khususnya saat guru memberikan Quis kepada siswa. Guru dapat memanfaatkan aplikasi ini untuk menarik perhatian siswa dalam mengerjakan Quis. Tampilan Quizziz yang menarik diharapkan dapat memotivasi siswa untuk belajar khususnya dalam kegiatan evaluasi. Quizizz adalah aplikasi yang dapat dioperasikan di smartphone dan laptop. Hal ini menjadikan siswa dapat mengikuti quis dengan aplikasi quizziz dengan baik. Dalam quizziz siswa berperan sebagai pemain seperti halnya dalam game, sehingga siswa tertarik untuk mengerjakan quizziz. Ini berarti suasana game yang dibawa dalam ruangkelas, sehingga menjadikan pembelajaran dalam kelas lebih menyenangkan dan lebih interaktif. Quizizz merupakan aplikasi berupa kuis interaktif yang dianggap mampu menarik minat siswa karena menggantikan cara lama kuis yang hanya melibatkan kertas dan pulpen atau pensil. Peserta pengabdian masyarakat ini adalah para guru SMP Labschool Unesa 1 Surabaya 19 orang. SMP Labschool Unesa 1 Surabaya terletak di Kota Surabaya Provinsi Jawa Timur Metode kegiatan Pengabdian Masyarakat ini dilakukan melalui kegiatan pemaparan materi tentang memaksimalkan aplikasi Quizziz yang terintegrasi google classroom, Pelatihan dan dilanjutkan dengan latihan mandiri.

10.53625/jabdi.v1i6.520

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Kajian Kinerja Kompor Limbah Biomassa Padat Skala Industri Rumah Tangga

Seminar Nasional Teknik Kimia Kejuangan (2018)

Suhartono Suhartono Fitria Gasela Anis Khoirunnisa

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Computational design optimization: a combined darrieus-savonius water turbine design with a modified blade position on buckets

Suhartono Suhartono Irjan Irjan

The aim of this study was to determine the optimization of a combined Darrieus-Savonius water turbine design with a modified blade position on buckets. To determine the right position of the blade on the buckets, an analysis of the cut plot pressure on the combined turbine was required. The analysis of the cut plot pressure usually uses the embedded analysis in SolidWorks software to improve the combined water turbine design. This research seeks to propose an additional analysis of the cut plot pressure using an image-processing approach. The image processing approach in the analysis of the cut plot pressure was done by clustering image-based segmentation using the Fuzzy C-Means algorithm. The combined water turbine design used the NACA 0018 blade profile and buckets. Modification of the blades’ position on the buckets can improve the turbines’ performance. Three variations of the blades’ position modifications were employed, and three variations of the turbine design were simulated by SolidWorks software to obtain the cut plot pressure, which, in the form of a digital image on each variation, was analyzed using the Fuzzy C-Means algorithm. Variations of the combined water turbine design were analyzed and tested using SolidWorks and MATLAB software. The results of the study indicate that the variations of the blades’ position on the buckets can increase the pressure of the combined water turbine.

Position (finance)

Water turbine

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APPLICATION OF QUICK ON THE DRAW MODEL TO IMPROVE EXPOSITORY WRITING SKILLS FOR FIFTH GRADE STUDENTS OF SDN 2 KARANGPOH IN ACADEMIC YEAR OF 2020/2021

Kalam Cendekia Jurnal Ilmiah Kependidikan (2021)

Rifqi Aulia Rahman Suhartono Suhartono Tri Saptuti Susiani

<pre>The objectives of the study were: (1) to describe the application of Quick on the Draw model to improve expository writing skills; (2) to improve expository writing skills through the application of Quick on the Draw model; (3) to describe the obstacle and solution in implementing the Quick on the Draw model to improve expository writing skills. This collaborative classroom action research was conducted in three cycles. The subjects were the teacher and students of fifth grade of SDN 2 Karangpoh. The data were qualitative and quantitative data. Data collection techniques used observation, interviews, and tests. The data validity used triangulation of techniques and triangulation of sources. The data analysis included data reduction, data presentation, and conclusions drawing. The results showed that: (1) the steps of the Quick on the Draw model were: (a) delivering learning objectives, (b) preparing question cards, (c) creating group formation and learning material, (d) delivering rules of game, (e) having problem solving, (f) delivering results of discussion, (g) giving rewards, and (h) reviewing exam questions; (2) the application of the Quick on the Draw model improved expository writing skills; (3) the obstacle was the students focused on answering questions quickly but did not pay attention to the accuracy of the answers. The solution was the teacher guided the students to answer questions quickly and accurately.</pre>

Data presentation

Presentation (obstetrics)

Note-taking

Qualitative property

Academic year

10.20961/jkc.v9i2.46275

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Deep reinforcement learning in agricultural IoT-based: A review

E3S Web of Conferences (2024)

Indra Griha Tofik Isa Adhan Effendi Suhartono Suhartono

The world’s food needs have an impact on innovation in the field of agriculture, and one of them is by implementing deep reinforcement learning (DRL) technology, which is very relevant to the Industrial Revolution 4.0. This research discusses important issues and developments in DRLs that are implemented, especially in the field of IoT-based agriculture. The research method uses a Systematic Literature Review (SLR) approach through searching and analysing raw data sources, sorting and selecting relevant data relevant to the topics discussed, discussing topic areas and how trends are in current conditions, and concluding. The purpose of this study is to see how the current state of DRL implementation in agricultural IoT-based. The limitations of the study are that (1) the data sources come from Scopus-indexed journals; (2) the journal period is 2021–2023; (3) the research approach uses SLR; and (4) the focus of the discussion includes the implementation of DRL in agricultural IoT-based systems, the development of DRL technology, and the use of tools in DRL.

10.1051/e3sconf/202447907004

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HUBUNGAN KEDISIPLINAN GURU DENGAN KEDISIPLINAN BELAJAR PESERTA DIDIK KELAS V DI SD NEGERI SE-KECAMATAN KEBUMEN TAHUN AJARAN 2019/2020

Kalam Cendekia Jurnal Ilmiah Kependidikan (2020)

Umi Roudoh Suhartono Suhartono Ratna Hidayah

Penelitian ini bertujuan untuk menemukan ada tidaknya hubungan dan besarnya sumbangan antara kedisiplinan guru dengan kedisiplinan belajar peserta didik di SD Negeri se-Kecamatan Kebumen tahun ajaran 2019/2020.Jenis penelitian yang digunakan yaitu kuantitatif dengan metode penelitian korelasi. Data dikumpulkan dengan cara penyebaran angket kepada 335 peserta didik kelas V. Analisis data dengan uji korelasi sederhana dan sumbangan efektif (SE) dengan taraf signifikansi 5%. Hasil penelitian ini menunjukkan terdapat hubungan yang positif dan signifikan antara kedisiplinan guru dengan kedisiplinan belajar dengan nilai signifikansi 0,000 < 0,05. Nilai korelasi sebesar 0,481 yang berarti kedua variable memiliki hubungan positif. Nilai sumbangan efektif variabel profesionalisme guru bersertifikasi terhadap hasil belajar yaitu 23,1% dan sisanya 76,9% dipengaruhi oleh faktor lain. Berdasarkan hasil penelitian tersebut dapat disimpulkan bahwa terdapat hubungan positif dan signifikan antara kedisiplinan guru dengan kedisiplinan belajar peserta didik kelas V di SD Negeri se- Kecamatan Kebumen tahun ajaran 2019/2020. Kata kunci: kedisiplinan, kedisiplinan guru, kedisiplinan belajar peserta didik

10.20961/jkc.v8i2.41696

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Implementation of Convolutional Neural Network Algorithm for Detecting Empty Parking Area Based on Raspberry Pi

Jurnal E-Komtek (Elektro-Komputer-Teknik) (2024)

Suhartono Suhartono Satria Gunawan Zain Ayu Futri Sadaruddin

This study aims to implement a convolution neural network algorithm to detect empty parking areas based on Raspberry Pi 4 and use the Convolutional Neural Network (CNN) method of the YOLO V5 model. This research consists of several stages, starting from the potential and problem stages, needs analysis, literacy studies, building prototypes, system design, and system testing. The datasets collected were taken using smartphone cameras and webcams with a total of 645 image datasets which were divided into two categories, namely training data and validation. System testing is carried out in two conditions, namely during the day and at night. The results of the detection test for observing variations in the position of filled and unfilled vehicles obtained the highest average accuracy during daytime conditions, while for observing cars entering and leaving the parking lot during day and night conditions, the results were the same percentage of success.

Raspberry Pi

10.37339/e-komtek.v8i1.1220

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