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Bibliometrix Analysis: Trade Process Information Systems in Tangerang Modern Land Market Novita Trisetyo; Riki Riki
eCo-Buss Vol. 5 No. 3 (2023): eCo-Buss
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/eb.v5i3.824

Developing an information system that can facilitate the trading process at Pasar Modernland Tangerang. Pasar Modernland Tangerang is a modern market that has various types of traders and products offered to consumers. However, the trading process in this market still faces a number of obstacles, such as a lack of efficiency, lack of transparency, and difficulties in managing trade data. Therefore, this study aims to design and implement an information system that can help improve the trading process at Pasar Modernland Tangerang. Study This aim For develop system information that facilitates the trading process at Pasar Modernland Tangerang. Pasar Modernland Tangerang is a modern market that has various type merchants and products offered to consumer. The research method used in the given text is a systematic literature review. The text describes the process of conducting a bibliometric analysis to identify relevant publications for research. This study used a combination of keywords, filtering by time, type of publication, open access, language, and removal of duplicates to screen studies that fit the research objectives. This helps researchers narrow the range of studies to be synthesized and analyzed(Rojas-Sánchez et al., 2023). The information system designed and implemented in the study aimed to increase efficiency, strengthen transparency, and ease trading data management. The system covered several important features, such as trader registration and profile management, online ordering and payment, inventory management, and trading data reports and analysis. The system was expected to help increase the quality of service for consumers, make it easy for merchants to manage their business, and increase the potential growth of the economy in the market area

Bibliometrix Analysis: Management Inventory dan Supply Management Hariyanto Hariyanto; Riki Riki
eCo-Fin Vol. 5 No. 3 (2023): eCo-Fin
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/ef.v5i3.870

Inventory and Supply Management is an important aspect of efficient business operations. Both are interrelated and have an impact on achieving competitive advantage and customer satisfaction. In the case study of PT. Berkahjaya Sentosa Technique, Bibliometrix analysis is used to evaluate the performance of inventory and supply management of companies. The results show that effective inventory and supply management can improve operational efficiency and customer satisfaction. Bibliometrics is an analytical method used to map and analyze scientific literature in a particular field. In the context of inventory management and distribution control, bibliometrics can provide valuable insight into the development and focus of study in a particular field. The purpose of this study is to analyze and summarize scientific literature related to inventory management and distribution control using bibliometric methods. The method used is descriptive analysis using analysis tools such as three-field plots and tables of main information. The results show that scientific literature related to inventory management and distribution control has experienced a decline in annual growth in the period 2018-2023. However, the average citation per document shows that this literature is still very relevant and important in the context of inventory management and distribution control. This research provides valuable insights for researchers and practitioners in the field of inventory management and distribution control

Enhancing Consumer-to-Consumer (C2C) E-Commerce through Blockchain: A Model-Driven Approach Aditiya Hermawan; Oscar Hasan Putra; Junaedi Junaedi; Yusuf Kurnia; Riki Riki
ComTech: Computer, Mathematics and Engineering Applications Vol. 15 No. 1 (2024): ComTech
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/comtech.v15i1.10638

The rapid progress of Information and Communication Technology (ICT), especially the Internet, has changed lifestyles in profound ways, including sharing ideas, virtual interactions, digital entertainment, and online transactions. It has resulted in businesses globally turning to electronic commerce (e-commerce) to market products. E-commerce has revolutionized operations with features such as centralized storage and detailed product information in the marketing process. However, the inefficiency and lack of transparency in these centralized systems lead to high costs and limited user control, posing a significant challenge. Challenges include commission fees from E-Commerce providers, which hinder business growth. The research aimed to propose a more efficient and transparent model for Consumer-to-Consumer (C2C) e-commerce using blockchain technology. The C2C model enhanced user transactions and minimized third-party dependence, but centralization increased costs and limited seller access. Thus, a decentralized blockchain approach was proposed for greater transparency in e-commerce. The research innovatively applied blockchain to C2C e-commerce, enhancing market efficiency and transparency. The research method applied was a combination of Strengths, Weaknesses, Opportunities, and Threats (SWOT) analysis and practical application. The result shows that the approach succeeds in reducing high costs, transparency of data storage, and dependence on third parties. Blockchain reduces third-party involvement and promotes a fairer business environment because it uses a tamper-proof database for transparency, security, and efficiency in the ever-growing e-commerce ecosystem. Blockchain ensures automated transactions, real-time data tracking, and data security.

Adaptive Q-Learning for Safety Message Priority in Vehicular Fog Computing Based on Indonesia Transportation and Weather Data Ceng Giap Yo; Riki Riki; Aditiya Hermawana; Yusuf Kurniaa; Satria Abadi
International Journal of Artificial Intelligence Research Vol 10, No 1 (2026): June
Publisher : Universitas Dharma Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29099/ijair.v10i1.1718

The development of Intelligent Transportation Systems demands fast, adaptive, and reliable communication mechanisms between vehicles, especially when the system has to process multiple emergency events simultaneously. This article proposes an adaptation of the Adaptive Q-Learning model for the priority dissemination of safety messages in the Vehicular Fog Computing environment by utilizing the context of Indonesian data. Adaptation was made to the Q-learning design based on incident priority, but modified in terms of state, action, and reward variables to match the characteristics of Indonesia's urban transportation, especially DKI Jakarta and national data. The data sources mapped in the model come from the Central Statistics Agency's Land Transportation Statistics, Jakarta Statistics Profile, and BMKG Open Weather Forecast Data. State agents are built from a combination of bucket delay, traffic density level, weather conditions, trust node scores, and types of safety events such as ambulances, accidents, road hazards, and inundation. Action space is represented as a choice of different Quality of Service weights to balance delay, packet delivery ratio, trust, and energy efficiency. The reward function is designed to give higher priority to stacked emergencies while penalizing delays and energy consumption. The results in the tables and graphs in this article are presented as an illustrative simulation based on a methodology design, not the results of direct field tests. With this approach, this article offers a relevant, original, and contextual research framework for the development of intelligent transportation systems in Indonesia.

Marketplace Sebagai Sarana Pemasaran Jasa Kreatif: Studi Kasus Pada UMKM Robby Williams; Riki Riki
ALGOR Vol. 7 No. 2 (2026): Research for Future
Publisher : Fakultas Sains dan Teknologi-Universitas Buddhi Dharma

Show Abstract | Download Original | Original Source | Check in Google Scholar

Advances in information technology have driven major transformations in business, particularly through e-commerce and marketplaces that facilitate online transactions. One of the advantages is that buyers can order goods or services online without having to visit the store in person. However, data from the Central Statistics Agency shows that many businesses are still not involved in e-commerce, and the adoption of marketplaces is still lagging behind the use of instant messaging and social media. Based on this phenomenon, this study designed a web-based marketplace platform specifically for service products that can accommodate various business processes from different services. This application is expected to accommodate conventional service businesses by providing marketing facilities, expanding the reach of buyers, and facilitating the search for services. The system design uses the prototype method through the stages of needs analysis, prototype design, evaluation, coding, testing, and implementation. The results of the study show that the designed platform can accommodate various service business processes.

Deep Reinforcement-Driven Clustering and Routing Protocol for Smart Vehicular Networks Riki Riki; Setyawan Widyarto
International Journal of Artificial Intelligence Research Vol 9, No 2 (2025): December
Publisher : Universitas Dharma Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29099/ijair.v9i2.1576

This study proposes a Deep Reinforcement-Driven Clustering and Routing Protocol (DRCRP) to enhance energy efficiency and routing stability in smart vehicular networks. The protocol integrates an Actor–Critic deep reinforcement learning framework with Proximal Policy Optimization (PPO) to enable adaptive decision-making in dynamic Internet of Vehicles (IoV) environments. Through continuous learning, DRCRP adjusts cluster head selection and routing paths according to real-time vehicular mobility, residual energy, and link quality. Simulation experiments conducted using NS-2 and VanetMobiSim show that DRCRP achieves superior performance compared to benchmark algorithms such as AI-EECR, GWO-CH, and DMCNF. Quantitatively, the proposed model improved the Packet Delivery Ratio (PDR) by up to 4.3%, reduced End-to-End Delay by 18–22%, and lowered Energy Consumption by 12–16%. Moreover, DRCRP effectively minimized communication overhead and extended cluster head and member lifetimes, confirming its ability to balance reliability and energy efficiency. These results demonstrate the capability of reinforcement learning-based architectures to support intelligent, sustainable, and scalable vehicular communication systems under complex mobility conditions

Business Intelligent Method For Academic Dashboard Niki Destiandi; Aditiya Hermawan; Riki Riki
bit-Tech Vol. 1 No. 2 (2018): Data and Information Quality
Publisher : Komunitas Dosen Indonesia

Business Intelligence Lifecycle is a method for developing effective business intelligence (BI) decision support applications such as the Academic Dashboard. There are six steps in the BI life cycle from the beginning to implementation such as Justification, Planning, Business Analysis, Design, Construction, and Deployment, where each step is developed to be more detailed in accordance with BI's environmental needs (L. T. Moss). Management of tertiary institutions in Indonesia requires appropriate and fast academic reports that make it possible to make strategic decisions and in order to improve the quality of education. Academic evaluations can be presented with the dashboard being easy for decision making. The dashboard is a page that displays graphics as a KPI from an organization and provides everything needed to make key research results [4]. Problems that occur there are a lot of academic data that is stored but when turning it into a report at the time of evaluation academic activities are difficult and require a long time and require monitoring, evaluation and measurement tools that can measure the performance of universities. The Business Intelligence Lifecycle can be used to provide information to produce high resolution by adding KPI components.

Implementation of Naïve Bayes Algorithm for Classification of Mental Health of Social Media Users Aditiya Hermawan; Riki Riki
bit-Tech Vol. 4 No. 2 (2021): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v4i2.282

Social media has become a human need to interact in everyday life. Apart from being a means of communication, social media also has the additional function of exchanging information on the internet in various forms including writing, images and videos. One of the social media that has many users is Instagram, where Instagram offers information sharing features in the form of images, photos and short videos. The purpose of this feature is for users to express themselves and attract the attention of others, thereby creating feelings of happiness and increasing self-confidence. In addition to positive impacts, there are also negative impacts on users, for example excessive use that causes addiction so that it can cause mental health disorders. Mental health needs to be handled properly so that it does not continue to get worse, but there are several obstacles in seeing a psychiatrist in mental health, including limited access and also negative stigma if someone sees a psychiatrist. Therefore, a tool is needed that can be an early indication in knowing the level of mental agitation, especially in the use of Instagram. Classification in data mining can help provide initial information on a person's condition in his mental health. The Naïve Bayes algorithm provides an accuracy rate of 92.5% in classifying mental health on data sets that have been clustered. Good accuracy can help social media users know their mental health condition.

Implementation of Linear Regression Algorithm to Predict Stock Prices Based on Historical Data Jelvin Putra Halawa; Aditiya Hermawan; Junaedi .; Riki Riki
bit-Tech Vol. 5 No. 2 (2022): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v5i2.616

Stock investment is in great demand by investors because it can provide large profits with large risks or losses, in accordance with the investment principle of low risk low return, high risk high return. Stock prices that fluctuate in a very short time make it difficult for investors to predict stock prices in the future, so investors must pay more attention and gather as much information as possible regarding the shares to be bought or sold. This study aims to create a data mining model using a Linear Regression algorithm that can predict daily stock closing prices to provide information that supports investors in stock transactions. The data used is historical data on daily stock prices for 10 companies in the last 8 years for the period 25 February 2013 – 25 February 2021. Historical stock price data will be prepared using the Noving Average method and create a data mining model using the linear regression method to generate stock price prediction models. The resulting model can be used to predict stock prices well enough to assist investors in making investment decisions to obtain large profits with low risk.

Clustering Mental Health pada Pengguna Instagram Menggunakan Algoritma K-Means Yuliastati Putri Sugiarta Karlim; Aditiya Hermawan; Ardiane Rossi Kurniawan Maranto; Riki Riki
bit-Tech Vol. 6 No. 1 (2023): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v6i1.880

The use of Instagram too often can have an impact on the mental health of its users. Mental health that is not good requires early treatment so that it does not have a widespread impact on other health. Mental illness requires a professional to treat it as an effort to prevent a disease from getting worse. However, the stigma attached to sufferers is one of the significant causes behind the reluctance to seek treatment. Therefore we need a way so that Instagram users can find out for themselves the condition of their mental health. One way is to do Clustering the use of Instagram so that it can provide an early indication of a person's mental health. From the proposed model we can find out the categories of 600 respondents who were collected using a questionnaire with 10 main attributes. The proposed model is k-means with 3 clusters determined using the elbow method. In this study, the last centroid obtained through calculations was used to evaluate the k-means by comparing the results of the k-means calculations with the results of psychologists. The results of the K-means evaluation have an accuracy of 73.83% so that the last centroid can be applied to web-based applications that have been created. This mental health clustering model is expected to be able to help the community to get mental health conditions early and reduce the negative stigma that exists and can be used as evaluation material in using social media more wisely.

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