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Coping Strategies in Analyzing the Stress Impact of Online Gambling Addiction for Children and Adults Rizki, Akbar; Ishar, Meilia; Khairani, Khairani
Absorbent Mind Vol 4 No 2 (2024): Psychology and Child Development
Publisher : Institut Agama Islam Sunan Giri Ponorogo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37680/absorbent_mind.v4i2.5696

Increased internet accessibility has opened up opportunities for people to engage in various types of gaming, such as poker, online casinos, sports betting, and slot machines. This research aims to describe coping strategies for minimizing the stressful impact of online gambling addiction. This research uses a qualitative approach with a literature study. The results of this research reveal that online gambling addiction has a serious impact on people's stress levels in Indonesia. Factors such as financial losses, uncertainty of gambling outcomes, and interpersonal tension due to excessive involvement in online gambling activities are the main causes of increased stress levels. Coping strategies to minimize the stressful impact of online gambling addiction are achieved by focusing efforts on financial management, social support, and developing entertainment alternatives. The study found that dependence on online gambling triggers significant stress in both children and adults. The stress was caused by a variety of factors, including financial loss, impaired social relationships, and psychological impacts such as anxiety and depression.

Program OKE LUR! Sebagai Upaya Pencegahan Pernikahan Dini pada Remaja di Desa Karangtengah, Kabupaten Wonogiri Nabilah, Amelya Qois; Azizah, Farras Hanifah; Arnof, Naufal; Halim, Christin; Saputra, Eka Adi; Ranggabulawan, Gita Christy Ananda; Hananti, Shabira Fahria; Matondang, Harita Julie Zefanya; Alleluia, Rianti; Rizki, Akbar
Jurnal Pusat Inovasi Masyarakat Vol. 5 No. 2 (2023): Oktober 2023
Publisher : Direktorat Pengembangan Masyarakat Agromaritim, Institut Pertanian Bogor

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/jpim.5.2.240-250

Desa Karangtengah terletak di sebelah tenggara Kabupaten Wonogiri yang mempunyai luas wilayah 18.087,90 ha. Desa ini mempunyai sumber daya manusia produktif yang sangat tinggi tetapi mempunyai angka pernikahan dini yang tinggi. Program pencegahan pernikahan dini (OKE LUR: Ojo Kesusu Rabi Lur!) bertujuan untuk mengetahui penyebab tingginya angka pernikahan dini dan mengedukasi tentang pernikahan dini serta memotivasi untuk melanjutkan sekolah ke jenjang yang lebih tinggi. Program ‘OKE LUR!’ bekerjasama dengan puskesmas Kecamatan Karangtengah dan SMPN 1 Karangtengah. Pelaksanaan program dilakukan untuk seluruh siswa di SMPN 1 Karangtengah. Pengambilan data dilakukan dengan pre-test, post-test, dan wawancara. Materi yang disampaikan terkait dengan dampak dan bahaya pernikahan dini, cara pencegahan pernikahan dini, serta bahaya stunting. Latar belakang dari banyaknya kejadian pernikahan dini adalah kondisi perekonomian dan tingkat pendidikan orang tua, serta budaya yang ada di daerah ini. Berdasarkan hasil post-test, program ‘OKE LUR: Ojo Kesusu Rabi Lur!’ memberikan dampak positif terhadap siswa dan siswi SMPN 1 Karangtengah seperti bertambahnya pengetahuan terkait pernikahan dini dan bahayanya serta semangat untuk melanjutkan pendidikan ke jenjang yang lebih tinggi.

Application of Univariate and Multivariate Long Short Term Memory for World Crude Palm Oil Price Prediction : Penerapan Long Short Term Memory Peubah Tunggal dan Ganda untuk Prediksi Harga Minyak Kelapa Sawit Dunia Izzany, Nabil; Masjkur, Mohammad; Rizki, Akbar
Indonesian Journal of Statistics and Applications Vol 9 No 1 (2025)
Publisher : Statistics and Data Science Program Study, IPB University, IPB University, in collaboration with the Forum Pendidikan Tinggi Statistika Indonesia (FORSTAT) and the Ikatan Statistisi Indonesia (ISI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/ijsa.v9i1p10-20

Time series analysis is essential for predicting economic and other important factors; it can be done univariately or multivariately. Technological developments created long short term memory that can handle vanishing gradients and long-term dependencies. This research will predict the world price of crude palm oil because Indonesia, as the world's largest crude palm oil producer, is strongly influenced by the world crude palm oil price. This study uses monthly data on crude palm oil, soybean oil, and crude oil prices from January 2002 to May 2024 obtained from the World Bank Commodity Price Data. This research applies univariate and multivariate long short term memory to predicting crude palm oil prices. The use of long short term memory is because the data shows non-linear elements and high volatility. The input used for univariate long short term memory is the crude palm oil price, while multivariate long short term memory uses crude palm oil, soybean oil, and crude oil prices. The univariate long short term memory proved to be more effective in the case of world crude palm oil price prediction. This is proven by the lower mean absolute percentage error of 6,574% compared to the multivariate long short term memory of 6,689%. This univariate long short term memory uses a combination of hyperparameters: neuron 32, epoch 100, time steps 1, batch size 64, and learning rate 0,01.

Perbandingan Performa Arimax-Garch Dan Lstm Pada Data Harga Penutupan Saham PT Aneka Tambang Tbk (ANTM.JK) Suwarso, Dhiya Khalishah Tsany; Rizki, Akbar; Rahmi, Salsabila Dwi; Mahesa, Hakim Zoelva; Gunawan, Windi; Fitri, Zafira Ilma; Angraini, Yenni; Putri, Adelia; Nurhambali, Muhammad Rizky
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 12 No 3: Juni 2025
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.2025128756

Banyaknya data deret waktu dengan pola nonlinear dan memiliki volatilitas tinggi pada berbagai sektor membuat sulit untuk melakukan pemodelan klasik seperti Autoregressive Integrated Moving Average (ARIMA). Permasalahan ini dapat diatasi salah satunya dengan mengembangkan metode Autoregressive Integrated Moving Average with Exogenous- Generalized Autoregressive Conditional Heteroskedasticity (ARIMAX-GARCH) yang memanfaatkan kovariat eksternal, sehingga memberikan solusi lebih baik pada data yang tidak stasioner. Di sisi lain, metode deep learning seperti Long Short-Term Memory (LSTM) unggul dalam menangkap pola non-linear dan dependensi jangka panjang. Oleh karena itu, penelitian ini membandingkan performa ARIMAX-GARCH dan LSTM dalam memprediksi harga saham PT Aneka Tambang Tbk (ANTM.JK). Data mingguan penutupan harga saham ANTM.JK periode 1 Januari 2018 hingga 30 Oktober 2023 digunakan dalam penelitian ini. Pemodelan ARIMAX-GARCH dengan peubah kovariat berupa data harga nikel berjangka dunia digunakan karena terdapat pengaruh signifikan harga nikel terhadap harga penutupan saham ANTM.JK dan terdeteksi adanya heteroskedastisitas dalam model. Metode berbasis machine learning, LSTM digunakan karena metode ini dikenal memiliki akurasi prediksi yang baik. Pengolahan data dilakukan menggunakan bantuan software R-Studio dan Python. Hasil penelitian menunjukkan LSTM memiliki performa yang lebih baik dengan nilai MAPE sebesar 4,425%, nilai ini lebih kecil jika dibandingkan model terbaik ARIMAX(2,1,2)-GARCH(1,1) dengan MAPE 7,326%. Abstract The large number of time series data with nonlinear patterns and high volatility in various sectors makes it difficult to perform classical modeling such as Autoregressive Integrated Moving Average (ARIMA). This problem can be overcome by developing the ARIMA with Exogenous- Generalized Autoregressive Conditional Heteroskedasticity (ARIMAX-GARCH) that utilizes external covariates, thus providing a better solution to non-stationary data. On the other hand, deep learning methods such as Long Short-Term Memory (LSTM) excel in capturing non-linear patterns and long-term dependencies. Therefore, this study compares the performance of ARIMAX-GARCH and LSTM in predicting the stock price of PT Aneka Tambang Tbk (ANTM.JK). Weekly closing data of ANTM.JK stock price from January 1, 2018 to October 30, 2023 are used in this study. ARIMAX-GARCH modeling with covariate variables in the form of world nickel futures price data is used because there is a significant effect of nickel prices on the closing price of ANTM.JK shares and heteroscedasticity is detected in the model. Machine learning-based method, LSTM is used because this method is known to have good prediction accuracy. Data processing is done using R-Studio and Python software. The results show that LSTM has better performance with a MAPE value of 4.425%, this value is smaller than the best model ARIMAX(2,1,2)-GARCH(1,1) with a MAPE of 7.326%.

KOMPARASI TEKNIK UNDERSAMPLING DAN OVERSAMPLING PADA REGRESI LOGISTIK BINER DALAM MENDUGA FAKTOR DETERMINAN BERHENTI MEROKOK PENDUDUK LANJUT USIA Amelia, Reni; Indahwati; Erfiani; Fitrianto , Anwar; Rizki, Akbar
Jurnal TIMES Vol 10 No 2 (2021): Jurnal TIMES
Publisher : STMIK TIME

Teknik resampling adalah salah satu teknik pre-processing untuk menyeimbangkan distribusi data sehingga mengurangi efek distribusi kelas atau kategori yang tidak seimbang. Teknik resampling yang biasa digunakan adalah random oversampling dan random undersampling. Dalam penelitian ini, random oversampling digunakan untuk menyeimbangkan data dengan cara oversampling secara acak pada kelas minoritas (penduduk lansia yang berhenti merokok). Random undersampling digunakan untuk menyeimbangkan data dengan cara undersampling (mengeliminasi) secara acak kelas mayoritas (penduduk lansia yang masih merokok). Data yang telah diproses dengan resampling selanjutnya dilakukan pemodelan dengan model regresi logistik biner. Model regresi logistik biner dengan random undersampling merupakan model terbaik karena memiliki balanced accuracy terbesar. Peubah yang signifikan memengaruhi berhenti merokok adalah pendidikan, pekerjaan, akses internet, dan usia lansia.

Identifying Factors Affecting Waste Generation in West Java in 2021 Using Spatial Regression Djuraidah, Anik; Rizki, Akbar; Alfan, Tony
JTAM (Jurnal Teori dan Aplikasi Matematika) Vol 8, No 2 (2024): April
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jtam.v8i2.19664

Responsible consumption and production is the 12th of the seventeen SDGs which is difficult for developing countries to achieve due to high waste production. Indonesia is the second largest producer of food waste in the world. Garbage is solid waste generated from community activities. Population density is an indicator to estimate the amount of waste generated in an area. The choice of West Java Province as the research area is based on the fact that this Province has the second highest population density in Indonesia. This study aimed to determine the predictors/factors that influence waste production in the districts/cities of West Java Province. The data used in this study are total waste as a response variable and GRDP (gross domestic product), total spending per capita, average length of schooling, literacy rate, number of MSMEs (micro, small, and medium enterprises), and several recreational and tourism places, the number of people's markets, and the number of restaurants as predictors. The methods used in this research are spatial autoregressive regression/SAR, spatial Lag-X/SLX, and spatial Durbin/SDM. The results of this study show that the SAR is the best model with the lowest BIC (74.442) and pseudo-R-squared (0.7934). Factors that significantly affect total waste production are literacy levels, the number of MSMEs, the number of traditional markets, and the number of recreational and tourist places.

Analysis of Covid-19 Risk Perception Survey Result Using Generalized Structured Component Analysis: Analisis Hasil Survei Persepsi Risiko Covid-19 Menggunakan Generalized Structured Component Analysis Robert, Zahira Rahvenia; Rizki, Akbar; Susetyo, Budi; Amir, Sulfikar
Indonesian Journal of Statistics and Applications Vol 6 No 2 (2022)
Publisher : Statistics and Data Science Program Study, IPB University, IPB University, in collaboration with the Forum Pendidikan Tinggi Statistika Indonesia (FORSTAT) and the Ikatan Statistisi Indonesia (ISI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/ijsa.v6i2p336-347

The capital city of Indonesia, Jakarta, became the province with the highest number of Covid-19. Response this situation, LaporCovid-19 collaborate with the Social Resilience Lab, Nanyang Technological University conducted a survey to measure how Jakarta residents perceive the risk of Covid-19 from May 29 to June 20 2020. Factors of risk perception are variables that cannot be measured directly, so they are analyzed used a Structural Equation Modeling (SEM) approach, namely Generalized Structured Component Analysis (GSCA). The Likert scale used can be considered as interval or ordinal depending on the point of view of the theory built. Therefore, this study will compare the GSCA method with the nonlinear GSCA and evaluate six variables, namely risk perception, knowledge, information, health behavior , social capital, and economy. Evaluation of the overall model showed that the nonlinear GSCA model can explain the diversity of qualitative data better than the GSCA model with FIT > 0.9. Based on GSCA nonlinear model, information has significantly influence of knowledge, economy and social capital have a real reciprocal relationship, along knowledge and risk perception have significantly influence of health behavior.

Coping Strategies in Analyzing the Stress Impact of Online Gambling Addiction for Children and Adults Rizki, Akbar; Ishar, Meilia; Khairani, Khairani
Absorbent Mind Vol. 4 No. 2 (2024): Psychology and Child Development
Publisher : Institut Agama Islam Sunan Giri Ponorogo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37680/absorbent_mind.v4i2.5696

Increased internet accessibility has opened up opportunities for people to engage in various types of gaming, such as poker, online casinos, sports betting, and slot machines. This research aims to describe coping strategies for minimizing the stressful impact of online gambling addiction. This research uses a qualitative approach with a literature study. The results of this research reveal that online gambling addiction has a serious impact on people's stress levels in Indonesia. Factors such as financial losses, uncertainty of gambling outcomes, and interpersonal tension due to excessive involvement in online gambling activities are the main causes of increased stress levels. Coping strategies to minimize the stressful impact of online gambling addiction are achieved by focusing efforts on financial management, social support, and developing entertainment alternatives. The study found that dependence on online gambling triggers significant stress in both children and adults. The stress was caused by a variety of factors, including financial loss, impaired social relationships, and psychological impacts such as anxiety and depression.

OPEC Crude Oil Price Forecasting Using ARIMA with Ensemble Empirical Mode Decomposition Lutfiah Adisti, Tiara; Soleh, Agus M; Alamudi, Aam; Rahardiantoro, Septian; Rizki, Akbar
Indonesian Journal of Statistics and Applications Vol 9 No 2 (2025)
Publisher : Statistics and Data Science Program Study, SSMI, IPB University, in collaboration with the Forum Pendidikan Tinggi Statistika Indonesia (FORSTAT) and the Ikatan Statistisi Indonesia (ISI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/ijsa.v9i2p230-239

World crude oil prices fluctuate every day. One source of crude oil traded is oil from crude oil exporting countries that are members of the Organization of the Petroleum Exporting Countries (OPEC). In the total of 40% of world crude oil is produced by OPEC. This makes forecasting the price of crude oil OPEC’s policy very necessary in order to maintain world oil market stability. Fluctuating oil price data is made simpler and easier to interpret by applying the Ensemble Empirical Mode Decomposition (EEMD) method. The EEMD method decomposes the data into a number of Intrinsic Mode Functions (IMF) and residual of the IMF. In this study, the ARIMA forecasting model is compared using the original data and the decomposition results in the form of IMF components and IMF residuals. The comparison of the two methods is seen based on the overall and average MAPE value of the forecasting results in five time ranges. The EEMD-ARIMA method has an average MAPE value of 9.09% and standard deviation MAPE value of 7.39%. OPEC crude oil price forecast in January-August 2021 ranges from $42.22 to $60.6 per barrel. The final result of the analysis in this study shows that the ARIMA method with decomposition data (EEMD-ARIMA) is better than the ARIMA method using original data

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