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Determination of Sub-Contractors Using AHP and SAW Method Arif Zikri; Adam Syahputra Cristanto; Imelda Imelda
Journal of Systems Engineering and Information Technology (JOSEIT) Vol 1 No 1 (2022)
Publisher : Ikatan Ahli Informatika Indonesia

One factor in the durability of a contractor company is its sub-contractor. The management of sub-contractors for companies engaged in construction services is one aspect of the company's durability to be able to complete the project that the selection is doing manually by viewing one single file That is sent by the company who wants to be a sub-contactor, it is of course besides very troublesome also inefficient and time consuming quite a while. In determining the winner of the Procurement Committee is still having difficulty to determine the standard of the company that will become a sub-contractor, not to mention the assessment is subjective so that the elected candidate is not the best candidate. The methods used in this research use Analytical Hierarchy Process (AHP) for weighting of criteria and for calculation of value using Simple Additive Weighting (SAW) of the results of the research obtained from the SAW method used Obtained by the best sub-contractor of PT. 4Cipta with a total of 0.962 from 5 alternatives listed

Analisis Penggunaan AI dalam Keberhasilan Customer Experience Pengguna Aplikasi E-Commerce Shopee Zikry, Arief; Muhammad Bitrayoga; Siska Yulia Defitri; Akhmad Dahlan; Nina Dwi Putriani
Indo-Fintech Intellectuals: Journal of Economics and Business Vol. 4 No. 3 (2024): Indo-Fintech Intellectuals: Journal of Economics and Business
Publisher : Lembaga Intelektual Muda (LIM) Maluku

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54373/ifijeb.v4i3.1387

E-commerce has experienced rapid growth with the adoption of digital technology particularly in Southeast Asia. AI enables Shopee to provide personalized product recommendations, responsive customer service, and secure shopping experiences. This study analyzes the use of Artificial Intelligence (AI) technology in enhancing user experience on the Shopee e-commerce platform. The research employs a mixed-method approach involving 400 Shopee users in Indonesia. Product recommendation and user experience personalization both significantly increase user satisfaction. User experience personalization has a greater influence than product recommendations in increasing user satisfaction. The implications of this study suggest that AI not only enhances Shopee's operational efficiency but also strengthens customer satisfaction and loyalty, making it a key element in maintaining competitiveness in today's competitive e-commerce industry

Analisis Penggunaan AI dalam Keberhasilan Customer Experience Pengguna Aplikasi E-Commerce Shopee Arief Zikry; Muhammad Bitrayoga; Siska Yulia Defitri; Akhmad Dahlan; Nina Dwi Putriani
Indo-Fintech Intellectuals: Journal of Economics and Business Vol. 4 No. 3 (2024): Indo-Fintech Intellectuals: Journal of Economics and Business
Publisher : Lembaga Intelektual Muda (LIM) Maluku

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54373/ifijeb.v4i3.1387

E-commerce has experienced rapid growth with the adoption of digital technology particularly in Southeast Asia. AI enables Shopee to provide personalized product recommendations, responsive customer service, and secure shopping experiences. This study analyzes the use of Artificial Intelligence (AI) technology in enhancing user experience on the Shopee e-commerce platform. The research employs a mixed-method approach involving 400 Shopee users in Indonesia. Product recommendation and user experience personalization both significantly increase user satisfaction. User experience personalization has a greater influence than product recommendations in increasing user satisfaction. The implications of this study suggest that AI not only enhances Shopee's operational efficiency but also strengthens customer satisfaction and loyalty, making it a key element in maintaining competitiveness in today's competitive e-commerce industry

OPTIMALISASI PENJUALAN IKAN SALAI BERBASIS DIGITAL MARKETING PADA UMKM KELUARGA MAJU BERSAMA DI KABUPATEN LAHAT Fitriadi, Heri; Zikry, Arief; Sulkhan Nurfatih, Muhammad
Martabe : Jurnal Pengabdian Kepada Masyarakat Vol 8, No 2 (2025): MARTABE : JURNAL PENGABDIAN KEPADA MASYARAKAT
Publisher : Universitas Muhammadiyah Tapanuli Selatan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31604/jpm.v8i2.888-895

Pengabdian masyarakat yang dilakukan di UMKM Keluarga Maju Bersama, Kabupaten Lahat, bertujuan untuk meningkatkan kualitas dan daya saing produk ikan salai melalui penerapan inovasi teknologi dan penguatan manajemen usaha. Program ini juga menitikberatkan pada diferensiasi produk dan pembentukan identitas merek yang khas, sehingga produk ikan salai mampu menonjol di pasaran. Selain itu, proyek ini bertujuan untuk mendorong praktik bisnis berkelanjutan dengan memberikan edukasi kepada pemilik UMKM tentang teknik produksi yang efisien dan pengelolaan sumber daya. Permasalahan utama yang dihadapi meliputi rendahnya kualitas kemasan dan strategi pemasaran yang kurang efektif, sehingga produk sulit bersaing di pasar. Metode yang digunakan mencakup pendekatan kuantitatif dengan desain deskriptif, pengumpulan data melalui observasi, wawancara, dan kuesioner, serta analisis deskriptif dan inferensial secara statistik. Hasil penelitian menunjukkan bahwa penerapan rumah pengasapan dan vacuum sealer secara signifikan meningkatkan kualitas dan daya tarik produk, serta memperpanjang masa simpan ikan salai. Selain itu, pelatihan pemasaran digital meningkatkan pemahaman dan keterampilan mitra dalam mempromosikan produk mereka, sehingga terjadi peningkatan penjualan sebesar 50% dalam enam bulan. Dengan demikian, kegiatan pengabdian masyarakat ini berhasil menciptakan model pengembangan UMKM berbasis teknologi dan pemasaran digital.

Penentuan Calon Sub-Kontraktor menggunakan Metode Analytical Hierarchy Process (AHP) dan Simple Additive Weighting (SAW) Zikri, Arif; Cristanto, Adam Syahputra; Imelda, Imelda
MEANS (Media Informasi Analisa dan Sistem) Volume 5 Nomor 1
Publisher : LPPM UNIKA Santo Thomas Medan

One factor in the durability of a contractor company is its sub-contractor. The management of sub-contractors for companies engaged in construction services is one aspect of the company's durability to be able to complete the project that the selection is doing manually by viewing one single file That is sent by the company who wants to be a sub-contactor, it is of course besides very troublesome also inefficient and time consuming quite a while. In determining the winner of the Procurement Committee is still having difficulty to determine the standard of the company that will become a sub-contractor, not to mention the assessment is subjective so that the elected candidate is not the best candidate. The methods used in this research use Analytical Hierarchy Process (AHP) for weighting of criteria and for calculation of value using Simple Additive Weighting (SAW) of the results of the research obtained from the SAW method used Obtained by the best sub-contractor of PT. 4Cipta with a total of 0.962 from 5 alternatives listed

Penerapan Whale Optimization Algorithm dalam Pengoptimalan Portofolio Investasi Menggunakan Model Prediktif Artificial Intelligence Mediansyah, Iski; Septian, Firza; Zikry, Arief
Jurnal Software Engineering and Computational Intelligence Vol 2 No 01 (2024)
Publisher : Informatics Engineering, Faculty of Computer Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36982/jseci.v2i01.4147

The optimization of investment portfolios has become a primary focus in the management of dynamic financial markets. The Whale Optimization Algorithm (WOA) and Artificial Intelligence (AI) have emerged as potential solutions to tackle market complexities. WOA offers an efficient approach to finding optimal solutions, while AI models such as Artificial Neural Networks (ANN) and Machine Learning (ML) algorithms are effective in predicting market behaviors. The integration of WOA and AI holds promise for better outcomes in optimizing investment portfolios by considering complex factors and market volatility. However, the development of this technology requires interdisciplinary collaboration, increased financial and technological literacy, and consideration of social and environmental aspects. With a sustainable, inclusive, and responsible approach, we can create a more sustainable financial future that positively impacts society and the environment.

OPTIMALISASI PENJUALAN IKAN SALAI BERBASIS DIGITAL MARKETING PADA UMKM KELUARGA MAJU BERSAMA DI KABUPATEN LAHAT Fitriadi, Heri; Zikry, Arief; Sulkhan Nurfatih, Muhammad
Martabe : Jurnal Pengabdian Kepada Masyarakat Vol 8, No 2 (2025): MARTABE : JURNAL PENGABDIAN KEPADA MASYARAKAT
Publisher : Universitas Muhammadiyah Tapanuli Selatan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31604/jpm.v8i2.888-895

Pengabdian masyarakat yang dilakukan di UMKM Keluarga Maju Bersama, Kabupaten Lahat, bertujuan untuk meningkatkan kualitas dan daya saing produk ikan salai melalui penerapan inovasi teknologi dan penguatan manajemen usaha. Program ini juga menitikberatkan pada diferensiasi produk dan pembentukan identitas merek yang khas, sehingga produk ikan salai mampu menonjol di pasaran. Selain itu, proyek ini bertujuan untuk mendorong praktik bisnis berkelanjutan dengan memberikan edukasi kepada pemilik UMKM tentang teknik produksi yang efisien dan pengelolaan sumber daya. Permasalahan utama yang dihadapi meliputi rendahnya kualitas kemasan dan strategi pemasaran yang kurang efektif, sehingga produk sulit bersaing di pasar. Metode yang digunakan mencakup pendekatan kuantitatif dengan desain deskriptif, pengumpulan data melalui observasi, wawancara, dan kuesioner, serta analisis deskriptif dan inferensial secara statistik. Hasil penelitian menunjukkan bahwa penerapan rumah pengasapan dan vacuum sealer secara signifikan meningkatkan kualitas dan daya tarik produk, serta memperpanjang masa simpan ikan salai. Selain itu, pelatihan pemasaran digital meningkatkan pemahaman dan keterampilan mitra dalam mempromosikan produk mereka, sehingga terjadi peningkatan penjualan sebesar 50% dalam enam bulan. Dengan demikian, kegiatan pengabdian masyarakat ini berhasil menciptakan model pengembangan UMKM berbasis teknologi dan pemasaran digital.

Performance Optimization of Document Clustering for Harry Potter Series Comments using Cosine Similarity Septian, Firza; Zikry, Arief; Dwi Putriani, Nina
Journal of Intelligent Systems and Information Technology Vol. 1 No. 1 (2024): January
Publisher : Apik Cahaya Ilmu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61971/jisit.v1i1.30

This research delves into the distinctive realm of comment clustering, focusing on the extensive discourse generated by the Harry Potter series. Leveraging a dataset from Kaggle, the study aims to optimize document clustering using cosine similarity within the K-Means algorithm. The research addresses the nuanced dynamics of sentiment and preferences within the Harry Potter fan community. A comprehensive methodology involves data collection, preprocessing, TF-IDF initialization, K-Means clustering with varying distance metrics, and result evaluation. The dataset of 491 respondents unveils diverse gender, geographical, and age distributions, adding complexity to the analysis. The K-Means clustering results highlight predominant positive sentiment, emphasizing the enduring popularity of the series. The study's originality lies in its focus on the Harry Potter cultural phenomenon, contributing to sentiment analysis and fan engagement discourse. The implications extend to researchers, practitioners, and enthusiasts seeking a deeper understanding of online discussions surrounding iconic media franchises.

Penerapan KNN, DT, dan NB untuk Memprediksi Task Success Developer Berbasis AI-Metrics Iski Mediansyah; Muhammad Bitrayoga; Arief Zikry; Firza Septian
BETRIK Vol. 16 No. 02 (2025): Jurnal Ilmiah BETRIK : Besemah Teknologi Informasi dan Komputer
Publisher : PPPM Institut Teknologi Pagar Alam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36050/rsvfdr22

This study is motivated by the limited utilization of AI-based metrics to predict task success among developers in software development projects. The main issue addressed is the absence of a systematic comparative approach to classification algorithms in identifying the most effective model in this context. Therefore, this research compares the performance of three classification algorithms—K-Nearest Neighbors (KNN), Decision Tree (DT), and Naïve Bayes (NB)—in predicting task success using AI-metrics data. The evaluation metrics include precision, recall, F1-score, and accuracy, presented through classification reports and confusion matrices. The results show that DT achieved an accuracy of 91%, KNN 92%, and NB 86%. The confusion matrix analysis indicates that DT demonstrates high precision, KNN shows minor imbalance, and NB struggles to identify minority classes. Additionally, clustering was performed using the K-Means algorithm and visualized in two dimensions through Principal Component Analysis (PCA), revealing clear segmentation among developer groups. The ultimate benefit of this study is to provide a foundation for decision-making in selecting the most appropriate algorithm to enhance developer team effectiveness and personalize managerial strategies. The novelty of this research lies in the combined application of classification and clustering approaches using AI-metrics to more accurately and datadrivenly identify developer task success.

AI-Powered GRU untuk Prediksi Saham Syariah Indonesia Berbasis Analisis Multi Faktor Selvy Megira; Arief Zikry; Nina Dwi Putriani
BETRIK Vol. 17 No. 02 (2026): Jurnal Ilmiah BETRIK : Besemah Teknologi Informasi dan Komputer
Publisher : PPPM Institut Teknologi Pagar Alam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36050/5dfrdm67

This study aims to develop a predictive model for Indonesian Sharia stock prices using the Gated Recurrent Unit (GRU) algorithm with a multi-factor analysis approach. The main challenge in analyzing Sharia stocks lies in the high volatility influenced by fundamental, technical, bandarmology, and macroeconomic factors. GRU was chosen because it has a simpler structure compared to LSTM while remaining effective in processing complex time-series data. The dataset includes variables such as EPS, PER, PBV, ROA, ROE, MA, RSI, MACD, foreign buy/sell, and the IHSG index, normalized using the Min-Max Scaler. The results show that the GRU model achieves high predictive accuracy, with a MAPE of 0.0286 and an RMSE of 74.92. Visualizations including training vs validation loss, scatter plots, residual plots, and error distribution confirm that the model avoids overfitting and generalizes well. Furthermore, the deployment of an interactive interface based on Gradio enables real-time prediction simulations, making this research not only academically significant but also practically useful for investors and policymakers in the Sharia capital market.

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