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Journal : Green Intelligent Systems and Applications

Analysis of Effectiveness in the Utilization and Control of Electronic Waste (E-Waste) in Indonesia Savitri Amalia; Ibrahim Amyas Aksar Tarigan; Anita Rizkiyani; Catur Apriono
Green Intelligent Systems and Applications Vol. 1 Iss. 1 (2021)
Publisher : Tecno Scientifica Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (641.361 KB) | DOI: 10.53623/gisa.v1i1.29

Abstract

In Indonesia, E-waste continues to grow rapidly, along with the increasing use of electronic goods such as telecommunications devices, households, offices, etc. Although it can be recycled, only a small portion can be done, and the recycling process is still under minimal control. Most E-waste is categorized as hazardous and toxic material waste. E-waste has a very high hazard impact if it is not recycled properly and correctly, such as polluting, damaging, and endangering the environment. This article uses forecasting of e-waste growth and canalization e-waste in Indonesia. The first data was obtained from EWasteRJ, a social community engaged in e-waste collection. The second data is obtained from questionnaires distributed to 110 respondents, focusing on knowledge and ways of handling E-waste. Using statistical analysis on both data shows that the amount of E-waste in Indonesia continues to increase every year, and public awareness of the dangers of E-waste is increasing.
The Potential of Smart Farming IoT Implementation for Coffee farming in Indonesia: A Systematic Review Aditya Eka Mulyono; Priska Apnitami; Insani Sekar Wangi; Khalfan Nadhief Prayoga Wicaksono; Catur Apriono
Green Intelligent Systems and Applications Vol. 2 Iss. 2 (2022)
Publisher : Tecno Scientifica Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53623/gisa.v2i2.95

Abstract

As one of Indonesia’s main export agricultural commodities, coffee farming faces many obstacles, ranging from plant pest organisms to climate and environmental problems. These problems can be solved using smart farming technology. However, smart farming technology has not been applied intensively in Indonesia. This paper aims to analyze the potential for implementing smart farming for coffee in Indonesia. This article presents a systematic review of the information about the potential application of IoT smart farming for coffee farming in Indonesia by applying the PSALSAR (Protocol, Search, Appraisal, Synthesis, Analysis, Report) review method. This study concludes the list of smart farming technologies for coffee that have the potential to be applied in Indonesia. Those technologies are classified based on their application scope: quality control (including subtopics like coffee quality control), climate monitoring, the anticipation of pest organisms, and coffee processing), coffee production planning, and coffee waste utilization. Regarding infrastructure readiness and the need for smart farming technology for coffee, the island of Java has the most potential for implementing smart farming for coffee as soon as possible. The high potential for application in Java is because the telecommunications technology infrastructure is ready, and the land area and coffee production are large.
Comparative Study of Base Transceiver Stations Infrastructure Planning Using Machine Learning for Under Construction Area: A Case Study of Ibu Kota Nusantara Yustin, Alfiyah Shaldzabila; Apriono, Catur
Green Intelligent Systems and Applications Volume 4 - Issue 2 - 2024
Publisher : Tecno Scientifica Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53623/gisa.v4i2.457

Abstract

Communication is a fundamental human need that occurs directly or through technologies like telephones and signal transmitters such as BTS and satellites. Satellites, including Starlink, serve as additional solutions for internet access needs, particularly in remote areas, albeit higher costs remain a factor necessitating conventional BTS infrastructure development. Telecommunication operators face challenges in constructing BTS in areas with limited access and complex financial considerations due to low demand in rural areas, requiring careful planning. This study utilizes several supporting variables with the aid of machine learning techniques such as Linear Regression, SVR, Random Forest, and Gradient Boosting to forecast BTS requirements. Comparative analysis shows that the random forest machine learning method provides the best modeling results compared to linear regression, Gradient Boosting, and SVR methods. Despite the superior performance of the random forest method, further fine-tuning is still needed through parameter adjustments and evaluation of variables used to achieve an even better model. The modeling results can be utilized to predict the BTS infrastructure needs in IKN, estimated at 61,135 units. In BTS development planning, mobile operators can collaborate both among themselves and with Internet Service Providers (ISPs) utilizing satellite media. Utilizing shared towers can be an option for cost-efficient BTS infrastructure development.
Radiation Performance Comparison and Analysis of Ku-band Microstrip Antennas with Diamond, Octagonal, and Circular Array Configurations Adriansyah, Muhammad Athallah; Wahdiyat, Aditya Inzani; Apriono, Catur
Green Intelligent Systems and Applications Volume 4 - Issue 2 - 2024
Publisher : Tecno Scientifica Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53623/gisa.v4i2.502

Abstract

Phased array antennas are essential in modern communication systems, particularly within the Ku-band, which is widely used for satellite communications and radar applications due to its high data rate capabilities. This paper explores the radiation characteristics of Ku-band microstrip antennas arranged in diamond, octagonal, and circular arrays, focusing on uniform excitation to ensure consistency across evaluations. Using CST Microwave Studio 2024 for simulations, the study found that the rectangular array provides the highest gain and narrowest beamwidth, making it suitable for applications where directional accuracy is critical. However, this configuration also resulted in higher sidelobe levels, which can be problematic in environments where minimal interference is required. The diamond array, while exhibiting lower gain, achieved superior sidelobe suppression, making it ideal for scenarios where reducing interference is prioritized over maximizing directivity. The octagonal and circular arrays provided balanced performance across all metrics, offering versatile options for various operational needs. These results provide valuable guidance for optimizing phased array designs to meet specific requirements in Ku-band applications.
Machine Learning Predictive Models Analysis on Telecommunications Service Churn Rate Akbar, Teuku Alif Rafi; Apriono , Catur
Green Intelligent Systems and Applications Volume 3 - Issue 1 - 2023
Publisher : Tecno Scientifica Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53623/gisa.v3i1.249

Abstract

Customer churn frequently occurs in the telecommunications industry, which provides services and can be detrimental to companies. A predictive model can be useful in determining and analyzing the causes of churn actions taken by customers. This paper aims to analyze and implement machine learning models to predict churn actions using Kaggle data on customer churn. The models considered for this research include the XG Boost Classifier algorithm, Bernoulli Naïve Bayes, and Decision Tree algorithms. The research covers the steps of data preparation, cleaning, and transformation, exploratory data analysis (EDA), prediction model design, and analysis of accuracy, F1 Score, receiver operating characteristic (ROC) curve, and area under the ROC curve (AUC) score. The EDA results indicate that the contract type, length of tenure, monthly invoice, and total bill are the most influential features affecting churn actions. Among the models considered, the XG Boost Classifier algorithm achieved the highest accuracy and F1 score of 81.59% and 74.76%, respectively. However, in terms of efficiency, the Bernoulli Naïve Bayes and Decision Tree algorithms outperformed XG Boost, with AUC scores of 0.7469 and 0.7468, respectively.
Co-Authors Adhiyoga , Yohanes Galih Adhiyoga, Yohanes Galih Aditya Eka Mulyono Admaja , Awangga Febian Surya Adriansyah, Muhammad Athallah Aini Nurul Akbar, Anshari Akbar, Teuku Alif Rafi Amirsyah Rayhan Mubarak Amirul Ihsan Anita Rizkiyani ATHALLAH, MIKAIL FAUZAN Awangga Febian Surya Admaja A’isya Nur Aulia Yusuf Dian Widi Astuti, Budi Irawan Prima Putra, Dian Widi Astuti, Dinari Nikken Sulastrie Sirin Dzakwan Widyo Pangestu Eko Tjipto Rahardjo EkoTjipto Rahardjo, EkoTjipto Ellin Devihana Pratiwi Ellin Devihana Pratiwi Ellin Devihana Pratiwi Farhan Annur Mahmudi Fauzan, Itsnanta Muhammad Fitri Yuli Zulkifli Hendrik Amrico Hendrik Amrico Hendry Steven Marbun Herma Adis Herry Tony Andhyka Ibrahim Amyas Aksar Tarigan Ihsan, Amirul Insani Sekar Wangi Itsnanta Muhammad Fauzan Khalfan Nadhief Prayoga Wicaksono Khalfan Nadhief Prayoga Wicaksono KURNIAWAN, FAROHAJI LUBIS, MUHAMMAD FIRDAUS SYAWALUDIN Mahmudi, Farhan Annur Manzila Izniardi Djomiy Martiana Kholila Fadhil May Hendra Panjaitan Mubarak, Amirsyah Rayhan Muh Asnoer Laagu Muhammad Afiff Rizki Muhammad Afiff Rizki Muhammad Riza Darmawan Muslim , Muslim Muslim Muslim Nabila Syadzwina Effendi Norihisa Hiromoto, Norihisa NUGROHO, SALSABILA SHITA PUTRI Nurul Aini Priska Apnitami Rahman, Liani Budi Resti Savira Resti Savira Roki Fernando RUHIYAT, ABDURRASYID Rusdiyanto, Dian Sahid Ridho Savitri Amalia Sifa Novwidia Agni Syaniri Andra Teuku Alif Rafi Akbar Trivia Anggita Wahdiyat, Aditya Inzani Wicaksono, Khalfan Nadhief Prayoga Yundari, Yundari Yus Natali Yustin, Alfiyah Shaldzabila