Garuda - Garba Rujukan Digital

Enhancing Coffee Marketing Strategies through Multi-Criteria Decision-Making Andryana, Septi; Wahyuddin, Mohammad Iwan; Rahman, Ben; Putra, Abdul Rahman Wijaya; Mantoro, Teddy
JOIV : International Journal on Informatics Visualization Vol 9, No 4 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.4.3282

Coffee is a globally preferred beverage, and Indonesia, as a major supplier, provides a wide variety of high-quality coffee varieties with unique characteristics from each region. East Manggarai, East Nusa Tenggara, Indonesia, produces Colol coffee, a high-quality variety with unexplored market potential. The marketing of Colol coffee faces significant challenges, including limited accessibility, lack of market information, and inadequate logistics infrastructure. A comprehensive marketing strategy necessitates the consideration of numerous criteria, which generate a range of alternative decisions to identify the marketing area. This study proposes a framework to optimize the marketing strategy of Colol coffee using the MCDM (Multi-Criteria Decision-making) approach, which integrates AHP, SMARTER, and TOPSIS methods. This framework is applied to rank marketing areas in 38 provinces in Indonesia based on five criteria, namely, accessibility, market potential, logistics, environmental conditions, and safety. The results show that the MCDM method can increase the effectiveness of marketing strategies. The top three alternative coffee marketing regions are Papua, East Kalimantan, and South Papua, with eigenvalues of 0.0569, 0.0424, and 0.0421. With incomplete data, in some marketing areas, it is a challenge to integrate multiple MCDM methods to have a better ranking that represents the real world of marketing strategy. This study supports the enhancement of the digital economy in the agricultural sector. It provides a meaningful understanding of the application of MCDM in marketing agricultural products, with far-reaching implications for marketing strategies in similar sectors.

Sentiment Analysis for the Brazilian Anesthesiologist Using Multi-Layer Perceptron Classifier and Random Forest Methods Asian, Jelita; Dholah Rosita, Moneyta; Mantoro, Teddy
JOIN (Jurnal Online Informatika) Vol 7 No 1 (2022)
Publisher : Department of Informatics, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/join.v7i1.900

Sexual harassment is defined as giving sexual attention both verbally, either in speech or writing, and physically to victims who are predominantly women, On July 13, 2022, there was a tweet featuring a video of sexual harassment that made it trend in various countries. The video irritated Twitter users and made various comments resulting in various sentiments that can be analyzed using sentiment analysis. The purpose of this study is to see what the public thinks about the sexual harassment case of Brazilian anesthesiologist. Besides the sentiment analysis, another aim of this study is to see how objective are those sentiments based on their polarity. This study uses a comparison of two methods in sentiment analysis, namely Multi-Layer Perceptron Classifier and Random Forest, and labeling automatically using TextBlob.Â This results in 94.44% accuracy, 94.44% precision, 92% recall and 93% f1_score. For MLP Classifier and accuracy 96.42%, precision 94.44%, recall 96.66% and f1_score 95.56% for Random Forest. Sentiment polarity score from the TextBlob is -0.5 and subjectivity is 0.4 which indicates that most statements are negative and subjective score is 0.4, which means those sentiments are subjective in nature.

Social Network Analysis: Identification of Communication and Information Dissemination (Case Study of Holywings) Aditiawarman, Umar; Lumbia, Mega; Mantoro, Teddy; Ibrahim, Adamu Abubakar
JOIN (Jurnal Online Informatika) Vol 8 No 1 (2023)
Publisher : Department of Informatics, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/join.v8i1.911

Social media especially Twitter has been used by corporation or organization as an effective tool to interact and communicate with the consumers. Holywings is one of the popular restaurants in Indonesia that use social media as a tool to promote and disseminate information regarding their products and services. However, one of their promotional items has gone viral and invited public protests which turned into a trending topic on Twitter for a couple of weeks. Holywings allegedly improperly promoted their products by using the most honorable names, “Muhammad” and “Maria”. Social network analysis of Twitter data is conducted to identify and examine information circulating among the users, which leads to wider public attention and law enforcement. In this study, we focused on the conversation about Holywings on Twitter from 24 June to 31 July 2022. The analysis was carried out using Python to retrieve data and Gephi software to visualize the interactions and the intensity of the network group in viewing the spread of information. The findings reveal the centrality account that caused the news to go viral are the CNN Indonesia (@CNNIndonesia) news media account and Haris Pertama (@knpiharis), with a centrality of 0.161 and 0.282, respectively. There are also 121 groups involved in the conversation with modularity of 0.821.

ENHANCING COFFEE PRODUCTION FACTOR ASSESSMENT USING LINEAR REGRESSION AND AHP FOR RELIABLE WEIGHT CONSISTENCY Aris Gunaryati; Teddy Mantoro; Septi Andryana; Benrahman; Mohammad Iwan Wahyuddin
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 11 No. 2 (2025): JITK Issue November 2025
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v11i2.6788

The agricultural sector, particularly coffee production, plays a crucial role in Indonesia’s economy as both a domestic commodity and an export product. However, efforts to optimize coffee production are often constrained by traditional Multi-Criteria Decision-Making (MCDM) methods that rely heavily on subjective judgments, leading to potential inconsistencies—especially in the presence of multicollinearity among variables. This study addresses that challenge by proposing a data-driven and consistent weighting method that integrates Multiple Linear Regression (MLR) with the Analytic Hierarchy Process (AHP). Regression coefficients derived from MLR—based on variables such as the area of immature (-0.2419), mature (0.8357), and damaged (0.5119) coffee plantations—are normalized and incorporated into the AHP pairwise comparison matrix. The resulting Consistency Ratio (CR) values are all below 0.1, indicating high internal consistency and statistical reliability of the derived weights. This integrated approach offers an objective and systematic foundation for MCDM in coffee production analysis, enhances the accuracy of agricultural planning, and supports evidence-based policymaking, while also providing a replicable model for addressing similar challenges in other sectors

Improving University Ranking Robustness Using Rank Geometric Weight Integration with CoCoSo Method for Reducing Ordinal Weighting Instability Andryana, Septi; Mantoro, Teddy; Mutiara, Achmad Benny; Ernastuti, Ernastuti; Prihandoko, Prihandoko; Gunaryati, Aris
Journal of Applied Data Sciences Vol 7, No 1: January 2026
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v7i1.1024

This study lies in the field of decision support systems, focusing on the application of Multi-Criteria Decision Making (MCDM) for ranking alternatives based on predefined organizational criteria. A persistent challenge in this domain is the instability and subjectivity of ordinal weighting methods - such as Rank Order Centroid (ROC), Rank Sum (RS), Rank Reciprocal (RR), and Rank Order Distribution (ROD), which derive weights solely from rank positions, often leading to inconsistent and unreliable outcomes. To address this, this study introduces Rank Geometric (RG) weights, a geometric mean aggregation of ROC, RS, RR, and ROD designed to reduce subjectivity, stabilize weight distribution, and enhance robustness. By using the Combined Compromise Solution (CoCoSo) method, the RG against Times Higher Education’s (THE) official weights were evaluated, and the four individual ordinal methods, applied to the top 10 Indonesian universities across five THE 2025 ranking criteria. Empirical results show that RG-CoCoSo produces stronger and more consistent correlations with THE’s rankings than THE-CoCoSo, as validated by Spearman and Pearson correlation tests, with a p-value of 0.0251. This study contributes a practical, data-driven weighting framework that strengthens the reliability of MCDM-based institutional performance evaluation and can be generalized to other ranking contexts.

Invisible watermarking as an additional forensic feature of e-meterai Rimbawa, H.A Danang; Alam, Sirojul; Saputro, Joko W.; Mantoro, Teddy
Indonesian Journal of Electrical Engineering and Computer Science Vol 41, No 1: January 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v41.i1.pp344-356

The e-meterai is an official digital product of the Indonesian government issued by the Directorate General of Taxation (DGT). Its usage has become increasingly widespread as conventional documentation transitions to digital formats, serving the same function as its printed counterpart. This product features a quick-response code embedded with unique Indonesian codes and offers overt, covert, and forensic features. This study aims to experiment with adding a forensic feature in the form of an invisible watermark. We employed two watermark embedding techniques, discrete Fourier transform (DFT) and scale-invariant feature transform (SIFT), to determine which is more suitable for this application. After embedding the watermark, we also simulate various attacks including gaussian noise, salt and pepper noise, averaging filter, rotation, translation, and speckle noise. For each attack, we calculated with normalized-cross correlation (NCC) values, obtaining 0.863 and 0.976 for the gaussian noise attack, 0.929 and 0.984 for the salt and pepper attack, 0.975 and 0.984 for the averaging filter attack, 0.173 and 0.097 for rotation attacks, 0.172 and 0.032 for translation attack, and 0.972 and 0.996 for speckle noise attack, using DFT and SIFT techniques, respectively.

Evading Antivirus Software Detection Using Python and PowerShell Obfuscation Framework Umar Aditiawarman; Alfian Dody; Teddy Mantoro; Haris Al Qodri Maarif; Anggy Pradiftha
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 22 No. 3 (2023)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v22i3.3088

Avoiding antivirus detection in penetration testing activities is tricky. The simplest, most effective, and most efficient way is to disguise malicious code. However, the obfuscation process will also be very complex and time-consuming if done manually. To solve this problem, many tools or frameworks on the internet can automate the obfuscation process, but how effective are obfuscation tools to avoid antivirus detection are. This study aimed to provide an overview of the effectiveness of the obfus- cation framework in avoiding antivirus detection. This study used experimental design to test and determine the effectiveness of the payload obfuscation process. The first step was generating Python and PowerShell payloads, followed by the obfuscation process. The results showed that by using the right method of obfuscation, malware could become completely undetectable. The automatic obfus- cation process also did not deteriorate the malwareâ€™s function. It was proven that the malware could run and open a connection on the server. These findings required more Python obfuscator techniques to determine the effectiveness of the obfuscated payload on the target machines using both static and dynamic analysis

Detecting Hidden Illegal Online Gambling on .go.id Domains Using Web Scraping Algorithms Muchlis Nurseno; Umar Aditiawarman; Haris Al Qodri Maarif; Teddy Mantoro
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 23 No. 2 (2024)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v23i2.3824

The profitable gambling business has encouraged operators to promote online gambling using black hat SEO by targeting official sites such as government sites. Operators have used various techniques to prevent search engines from distinguishing between genuine and illegal content. This research aims to determine whether websites with the go.id domain have been compromised with hidden URLs affiliated with online gambling sites. The method used in this research is an experiment using a FOFA.info dataset containing a complete list of 450,000 .go.id domains. A web scraping algorithm developed in Python was used to identify potentially compromised websites from the targeted listby analyzing gambling-related keywords in local languages, such as â€™slot,â€™ â€™judi,â€™ â€™gacor,â€™ and â€™togel'. The results showed that 958 of the 1,482 suspected.go.id sites had been compromised with an accuracy rate of 99.1%. This implies that security gaps have been exploited by illegal online gambling sites, posing a reputational risk to the government. Lastly, the scrapping algorithm tool developed in this research can detect illegal online gambling hidden in domains such as .ac.id, .or.id, .sch.id, and help authorities take necessary action.

ENHANCING COFFEE PRODUCTION FACTOR ASSESSMENT USING LINEAR REGRESSION AND AHP FOR RELIABLE WEIGHT CONSISTENCY Aris Gunaryati; Teddy Mantoro; Septi Andryana; Benrahman; Mohammad Iwan Wahyuddin
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 11 No. 2 (2025): JITK Issue November 2025
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v11i2.6788

The agricultural sector, particularly coffee production, plays a crucial role in Indonesia’s economy as both a domestic commodity and an export product. However, efforts to optimize coffee production are often constrained by traditional Multi-Criteria Decision-Making (MCDM) methods that rely heavily on subjective judgments, leading to potential inconsistencies—especially in the presence of multicollinearity among variables. This study addresses that challenge by proposing a data-driven and consistent weighting method that integrates Multiple Linear Regression (MLR) with the Analytic Hierarchy Process (AHP). Regression coefficients derived from MLR—based on variables such as the area of immature (-0.2419), mature (0.8357), and damaged (0.5119) coffee plantations—are normalized and incorporated into the AHP pairwise comparison matrix. The resulting Consistency Ratio (CR) values are all below 0.1, indicating high internal consistency and statistical reliability of the derived weights. This integrated approach offers an objective and systematic foundation for MCDM in coffee production analysis, enhances the accuracy of agricultural planning, and supports evidence-based policymaking, while also providing a replicable model for addressing similar challenges in other sectors

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