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Journal of Innovation and Future Technology (IFTECH)

iftech
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Published by Universitas Banten Jaya

ISSN : 26561719 EISSN : 26562774 DOI : 10.47080

Core Subject : Science, Education, Engineering,

Computer Science & IT Education Electrical & Electronics Engineering Engineering Library & Information Science

Jurnal IFTECH memiliki ruang lingkup mengenai hasil penelitian di bidang Komputerisasi Akuntansi, Teknik Informatika dan Manajemen Informatika (Ilmu Komputer dan Teknologi Informasi). Jurnal IFTECH merupakan salah satu media dokumentasi dan informasi ilmiah yang dapat dijadikan sebagai fasilitas untuk membantu para dosen, peneliti, staf dan mahasiswa dalam mempublikasikan dan menginformasikan hasil penelitian, gagasan, tulisan, karya ilmiah lainnya kepada masyarakat ilmiah.

Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)

Articles 199 Documents

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RANCANG BANGUN SISTEM INFORMASI PERMINTAAN LAYANAN DALAM LINGKUP DEPARTEMEN IT DI PT CONCORD CONSULTING INDONESIA Irma Yunita Ruhiawati; Dadang Amiruddin; Yul Hendra; Ely Nuryani; Nanang Wahyudi
Journal of Innovation And Future Technology Vol. 8 No. 1 (2026): Vol 8 No 1 (Februari 2026): Journal of Innovation and Future Technology (IFTECH
Publisher : LPPM Unbaja

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47080/iftech.v8i1.4522

In the digital era, information technology plays a vital role in supporting business operations. Effective IT service management is essential for improving work efficiency and employee productivity. PT Concord Consulting Indonesia, an information technology consulting firm, faces challenges in managing internal IT service requests, which are still handled manually through emails, instant messaging applications, and direct communication. This unstructured process leads to several issues, such as difficulties in tracking request statuses, lack of systematic documentation, absence of clear prioritization mechanisms, and unclear task distribution. This study aims to design and develop a web-based information system for managing IT service requests to support a more organized, well-documented, and transparent process. The implementation of this system is expected to accelerate service resolution, enhance operational efficiency, and improve coordination among teams within the IT Department.

KESIAPAN ETIKA PENGGUNAAN AI GENERATIF PADA TUGAS AKADEMIK: PENGARUH PEMAHAMAN INTEGRITAS AKADEMIK DAN PERSEPSI MANFAAT-RISIKO Eka Ramadhani Putra; Putri Ramadani; Fitri Safnita
Journal of Innovation And Future Technology Vol. 8 No. 1 (2026): Vol 8 No 1 (Februari 2026): Journal of Innovation and Future Technology (IFTECH
Publisher : LPPM Unbaja

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47080/iftech.v8i1.4526

Generative AI tools are increasingly used by students to support academic tasks such as drafting, coding, and summarizing. While these tools may improve efficiency and learning, they also introduce ethical risks related to academic integrity, transparency, privacy, and misinformation. This study examines ethical readiness for using generative AI in academic assignments and tests the effects of students' understanding of academic integrity and their perceived benefit-risk appraisal. A cross-sectional survey was administered to undergraduate students in semester 4 (N = 180). Data were analyzed using multiple regression. Key findings (simulated example): integrity understanding positively predicted ethical readiness (beta = 0.348, p <0.001), perceived risk also showed a positive effect (beta = 0.185, p = 0.013), while perceived benefit was not significant (beta = -0.053, p = 0.498).

TRANSFORMASI DATA TRANSAKSI KE DERET WAKTU DAN EVALUASI MODEL PERAMALAN PERMINTAAN PADA MARKETPLACE PLAZA BANTEN Widyawati Widyawati; Dadang Amiruddin
Journal of Innovation And Future Technology Vol. 8 No. 1 (2026): Vol 8 No 1 (Februari 2026): Journal of Innovation and Future Technology (IFTECH
Publisher : LPPM Unbaja

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47080/iftech.v8i1.4550

Plaza Banten, an MSME marketplace in Banten Province, generates ordering and sales transaction data that can be leveraged to support operational decisions, particularly inventory planning and promotional timing. However, decision-making is often reactive because demand forecasting has not been systematically developed from historical transactions. This study proposes an end-to-end pipeline that transforms Plaza Banten transaction records into daily demand time-series data at the product-category (Group) level, following data preparation and modeling stages in a data mining framework. The study uses transaction data from January to December 2024 and is positioned as a continuation of a previous Market Basket Analysis (MBA) study, which indicated that high transaction volumes were dominated by packaged rice products (e.g., rice boxes and chicken rice packages), motivating a forecasting follow-up for high-demand categories with recurring purchase patterns. The preprocessing stage includes data cleaning, validation of quantity and unit price, feature construction (quantity and revenue), daily demand aggregation by category, and completion of missing calendar dates to form continuous time series. For modeling, this study compares baseline forecasting methods (Naïve and 7-day Moving Average) against an Exponential Smoothing (Holt–Winters/ETS) model that accounts for trend and weekly seasonality. Model performance is evaluated using MAE, RMSE, and MAPE to ensure measurable selection of the best approach. The forecasting results are then interpreted as operational insights to estimate demand levels per category and support inventory planning and promotional prioritization based on predicted demand trends.

AUDIT MANAJEMEN INOVASI TEKNOLOGI INFORMASI MENGGUNAKAN FRAMEWORK COBIT 2019 DOMAIN APO04 PADA JT DIGITALLY Melinne Maldini Rosady; Ika Ima Nissa; Leo Sandi
Journal of Innovation And Future Technology Vol. 8 No. 1 (2026): Vol 8 No 1 (Februari 2026): Journal of Innovation and Future Technology (IFTECH
Publisher : LPPM Unbaja

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47080/iftech.v8i1.4554

In the current digital era, Information Technology has evolved beyond its traditional role as a mere supporting function and has become a strategic core that drives organizational competitiveness. Effective data governance is essential to improving data management practices, which ultimately contributes to enhanced organizational performance. JT Digitally is a company specializing in digital marketing agency services, offering solutions including digital marketing, software product development, and technology services. This study employs the COBIT 2019 Framework as its primary evaluation instrument, selected due to its focus on assessing capability and maturity levels based on designated target domains. The APO04 domain is employed to evaluate the company's Innovation Management System, encompassing the establishment and communication of quality standards across all relevant processes, procedures, and organizational outcomes. This research formulates strategic recommendations derived from the IT governance evaluation conducted at JT Digitally using the COBIT 2019 framework, specifically the APO04 domain. The capability assessment of APO04 subdomains shows an average score of 2.6 (Partially Achieved), indicating that implementation has begun but requires further strengthening. Recommendations include developing innovation governance policies and procedures, client-centered innovation management, and a systematic documentation

DETEKSI PENYAKIT PADA TANAMAN HORTIKULTURA MENGGUNAKAN ENSEMBLE LEARNING DAN VISION TRANSFORMER Muhammad Giza Aditya Nurdarmawan; Bagus Satrio Waluyo Poetro
Journal of Innovation And Future Technology Vol. 8 No. 2 (2026): Vol 8 No 2 (Agustus 2026): Journal of Innovation and Future Technology (IFTECH)
Publisher : LPPM Unbaja

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47080/iftech.v8i2.4578

Traditional tomato disease detection often relies on visual inspection by humans, making the process subjective, time-consuming, and susceptible to inconsistencies. This study proposes an automated image-based tomato leaf disease detection system using an ensemble learning approach that combines a Convolutional Neural Network (CNN) and a Vision Transformer (ViT) to improve classification performance. The model was developed to classify three categories of tomato leaves: Healthy, Leaf Mold, and Septoria Leaf Spot. The dataset was preprocessed through image resizing, normalization, and augmentation to improve model generalization. Predictions from the CNN and ViT models were integrated using a weighted ensemble strategy and deployed as a real-time web-based application using Gradio, enabling users to upload tomato leaf images and receive instant diagnostic results. Performance evaluation was conducted using accuracy, precision, recall, and F1-score. The proposed system achieved an overall accuracy of 81.60%. The model demonstrated excellent performance in identifying Healthy leaves, achieving 100% recall, and showed strong classification capability for Septoria Leaf Spot with an F1-score of 0.84. However, the system exhibited lower performance in detecting Leaf Mold, obtaining a recall of 0.46, indicating that this class remains challenging to distinguish from healthy leaves. Overall, the study demonstrates the feasibility of integrating deep learning models into an accessible diagnostic application while highlighting opportunities for future improvements in ensemble optimization and disease classification performance.

RANCANG BANGUN SISTEM INFORMASI KLASIFIKASI LIMBAH DOMESTIK BERBASIS WEB SERVICE MENGGUNAKAN ALGORITMA CNN Ahmad Surahmat; Rustam Effendy; Yul Hendra; Tb. Dedi Fua’dy; Fernanda Adytia Pratama
Journal of Innovation And Future Technology Vol. 8 No. 2 (2026): Vol 8 No 2 (Agustus 2026): Journal of Innovation and Future Technology (IFTECH)
Publisher : LPPM Unbaja

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47080/iftech.v8i2.4692

Disorganized domestic waste management has become a serious environmental issue due to the lack of systems for the rapid and automated recording of waste types. This study aims to design and develop a web service-based information system capable of automatically classifying domestic waste types. The research employs a Convolutional Neural Network (CNN) for waste image recognition, integrated with a web service architecture using a REST API to facilitate data exchange between systems. The dataset comprises thousands of domestic waste images categorized into major groups such as organic, plastic, paper, and metal. Key testing results demonstrate that the developed CNN model exhibits excellent and stable performance, achieving an accuracy rate of 89.03% and a loss value of 0.29 on the test data. The study concludes that integrating the CNN method with a web service is effective, accurate, and viable for automating domestic waste type identification, thereby supporting smarter environmental management systems.

IMPLEMENTASI METODE PERIODIC REVIEW DALAM SISTEM INFORMASI INVENTORY GUDANG BERBASIS WEB PADA PT NUFARM BOJONEGARA SERANG Muhammad Iqbal Fauqa Rijqi; Muhammad Sohari; Ahmad Roihan
Journal of Innovation And Future Technology Vol. 8 No. 2 (2026): Vol 8 No 2 (Agustus 2026): Journal of Innovation and Future Technology (IFTECH)
Publisher : LPPM Unbaja

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47080/iftech.v8i2.4771

Inventory management that is not properly controlled may result in inaccurate stock records, delays in inventory updates, and imbalances in stock availability, including shortages and excessive inventory levels. To address these issues, this research develops a web-based Inventory Information System by applying the Periodic Review method to optimize inventory management at PT Nufarm Indonesia, Bojonegara Serang Unit. The research employs a quantitative approach and follows the Waterfall software development model, encompassing requirement analysis, system design, implementation, and system testing. The application is built using PHP as the programming language and MySQL as the database management system. It provides functionalities for managing inventory data, supplier information, stock receipt and issuance transactions, as well as automated replenishment calculations based on the Periodic Review method. The proposed system is capable of monitoring inventory in real time and producing reorder recommendations by considering review intervals, demand rates, safety stock, and available inventory levels. System validation through White Box Testing produced a cyclomatic complexity value of 2, indicating that the program logic is efficient and performs as intended. Functional verification using Black Box Testing demonstrated that all system features satisfy the specified requirements. In addition, the User Acceptance Test (UAT) achieved an average score of 4.57, indicating a very good level of user satisfaction. The comparison between pre-test and post-test results also showed an increase in the average score from 65 to 87.67. These findings suggest that the developed system enhances inventory management efficiency while providing reliable support for procurement decision-making.

ANALISIS KINERJA DAN KEAMANAN RUNTIME AI AGENT GATEWAY OPENCLAW MENGGUNAKAN METODE PROGRESSIVE DISCLOSURE UNTUK OPTIMALISASI TOKEN Edi Suherlan; Aufa Dhia Ghaisani Suherlan
Journal of Innovation And Future Technology Vol. 8 No. 2 (2026): Vol 8 No 2 (Agustus 2026): Journal of Innovation and Future Technology (IFTECH)
Publisher : LPPM Unbaja

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47080/iftech.v8i2.4773

The rapid evolution of autonomous AI agent workflows has introduced new challenges in software engineering, particularly regarding context token bloat and runtime security vulnerabilities within AI agent gateways. This study presents a benchmarking-based secondary analysis and computational modeling of the Progressive Disclosure method for optimizing context management in the OpenClaw AI Agent Gateway. Rather than conducting direct experimental implementation, the evaluation synthesizes empirical benchmark data from previous Model Context Protocol (MCP) studies, mathematical performance modeling, and runtime threat analysis reported in recent literature. The benchmarking results indicate that Progressive Disclosure has the potential to reduce upfront token overhead by approximately 95.6%, decrease session token consumption from 45,000 to 2,800 tokens, reduce estimated execution latency by 60.9% (from 12.49 s to 4.88 s), and lower projected operational costs by 93.8%, while maintaining high tool-selection accuracy. Furthermore, the threat modeling analysis identifies several critical runtime vulnerabilities within the OpenClaw environment, including the Claw Chain attack sequence involving CVE-2026-44112, CVE-2026-44113, CVE-2026-44115, and CVE-2026-44118, which collectively demonstrate the importance of strengthening runtime isolation beyond prompt-level optimization. The analysis indicates that Progressive Disclosure can significantly improve context efficiency and reduce computational overhead; however, robust low-level virtualization mechanisms remain essential for protecting autonomous AI agent infrastructures against advanced runtime attacks.

PREDIKSI KEMAMPUAN PEMROGRAMAN BERDASARKAN NILAI ALGORITMA DAN STRUKTUR DATA MENGGUNAKAN METODE REGRESI LINIER BERGANDA Wahyuddin; Ahmad Kautsar; Darpi Darpi; Sawitri Nurhayati
Journal of Innovation And Future Technology Vol. 8 No. 2 (2026): Vol 8 No 2 (Agustus 2026): Journal of Innovation and Future Technology (IFTECH)
Publisher : LPPM Unbaja

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47080/iftech.v8i2.4793

Programming proficiency is a crucial core competence within the computer science education curriculum; however, students frequently encounter significant obstacles in mastering both logic and technical coding execution. This research aims to empirically analyze the influence of Algorithm and Data Structure course grades on students' programming ability using a quantitative approach through the Multiple Linear Regression method. The urgency of this study lies in the necessity for an accurate predictive model to identify students' academic performance at an early stage. This study utilizes simulated data from 100 students who have completed the relevant foundational courses. Statistical analysis results indicate that, simultaneously, Algorithm and Data Structure grades have a significant and positive impact on programming proficiency (p<0.05). Partially, the Data Structure variable contributes slightly more than the Algorithm variable, suggesting that efficient data organization is a critical determinant of practical programming quality. The research findings reveal a coefficient of determination (R2) of 0.8138, indicating that 81.38% of the variation in programming ability can be accurately explained by these two independent variables, while the remainder is influenced by other external factors. The conclusion of this study confirms that mastery of logical foundations and data organization serves as a primary predictor of programming success. Practically, educational institutions can implement this model as an early warning system to provide appropriate academic interventions for at-risk students, ultimately enhancing the quality of graduates in the information technology sector.

PEMANTAUAN TEMPAT SAMPAH CERDAS BERBASIS IOT DENGAN FITUR BERAT DAN KETINGGIAN SAMPAH DI UNIVERSITAS BANTEN JAYA Dedi Juniansha; Tubagus Dedy Fuady; Ahmad Surahmat; Rehulina Tarigan; Irma Yunita Ruhiawati
Journal of Innovation And Future Technology Vol. 8 No. 2 (2026): Vol 8 No 2 (Agustus 2026): Journal of Innovation and Future Technology (IFTECH)
Publisher : LPPM Unbaja

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47080/iftech.v8i2.4834

This study focuses on designing and implementing an automatic trash bin system based on the Internet of Things (IoT) at Banten Jaya University, utilizing the ESP32 as the central control unit. The system features an HC-SR04 ultrasonic sensor to assess the height of the waste and a 20 Kg Load Cell Weight Sensor to evaluate the weight of the trash. The testing results indicate that the ultrasonic sensor achieves 100% accuracy in measuring the trash height. The Load Cell sensor will track the volume of waste, and when the bin reaches capacity, the system will automatically alert the cleaning personnel. Furthermore, data transmission from the ESP32 to the IoT MQTT Panel application through the MQTT broker (broker.emqx.io) was executed successfully, with an average delay of merely 2 seconds. All components demonstrated stable and responsive functionality, allowing staff to monitor the volume, location, and pick-up schedule in real-time. Consequently, when the trash bin nears full capacity, the system will automatically signal the waste management center. This strategy not only optimizes waste transportation routes but also serves as an example of IoT application in a smart campus, helping to reduce the risk of pollution caused by waste accumulation.

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Contact Name
Ely Nuryani

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elynuryani@unbaja.ac.id

Phone
+6282114420019

Journal Mail Official
iftech@unbaja.ac.id

Editorial Address
https://ejournal.lppm-unbaja.ac.id/index.php/iftech/Master

Location
Kota serang,

Banten

INDONESIA