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All Journal SMARTICS Journal Jurnal Informatika Universitas Pamulang Julia Jurnal
Andri Triyono
Universitas An Nuur
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering

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Penerapan Least Squares Support Vector Machines (LSSVM) dalam Peramalan Indonesia Composite Index Andri Triyono; Rahmawan Bagus Trianto; Dhika Malita Puspita Arum
Jurnal Informatika Universitas Pamulang Vol 6, No 1 (2021): JURNAL INFORMATIKA UNIVERSITAS PAMULANG
Publisher : Teknik Informatika Universitas Pamulang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32493/informatika.v6i1.10237

Abstract

In the era of very rapidly advancing technology like today, both internet technology and computerization have made various corporate agencies or investors start thinking about the importance of the stock market in their capital division. Previously there were various purchases by the company's capital, such: gold, land, buildings, production machines, but at this time the purchase of capital shares should also start to attract attention and these purchases are legal investments. Various kinds of company shares that are sold can already be seen through the internet and it is very easy and attractive for companies that will make capital purchases, even the model can be chosen for both long-term and short-term capital purchases. This stock price forecasting system using the Least Squares Support Vector Machines (LSSVM) method will be very popular with investors to help determine conclusions for buying shares because it can reduce losses or even make the right decisions so that it will increase profits for investors or companies. Least Squares Support Vector Machines is a simpler model and has been modified from the previous model, namely: Support Vector Machines (SVM) method. Solving linear equations can be solved in a simpler way using LSSVM compared to using SVM. The variable used in the network is the close price variable. The kernel that used for this study is the RBF kernel. This study consists of three phases or stages. The first stage uses 400 historical data rows, second stage uses 800 historical data rows, and the third stage uses 1200 rows of data. This research obtains the best result of accuracy in the third stage. The third stage has the smallest MSE value: 0.00025248 by using 1200 rows of historical data.
Klasifikasi Rating Otomatis pada Dokumen Teks Ulasan Produk Elektronik Menggunakan Metode N-gram dan Naïve Bayes Rahmawan Bagus Trianto; Andri Triyono; Dhika Malita Puspita Arum
Jurnal Informatika Universitas Pamulang Vol 5, No 3 (2020): JURNAL INFORMATIKA UNIVERSITAS PAMULANG
Publisher : Teknik Informatika Universitas Pamulang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32493/informatika.v5i3.6110

Abstract

Online product ratings usually provide descriptive reviews and also reviews in the form of ratings. Likewise, what was done at the Lazada online store. Descriptive review can provide a clear view compared to a rating review to other potential buyers. However, in reality there is a mismatch between the description review and the rating given. This creates a lack of information for sellers as well as potential buyers. Automatic classification of buyer descriptive reviews is proposed in this study so that there is a match between descriptive reviews and rating reviews. This automatic classification descriptive review uses the Naive Bayes algorithm with n-gram feature extraction and TF-IDF word weighting. The results of this study obtained the best accuracy of 94.06%, a recall of 91.73% and precision of 90.71% in Bigram feature extraction. With this accuracy value it can be used as a reference or model for classifying product description reviews, so that the feedback process between sellers and buyers can run well.
Performance Evaluation of Agentic Workflow-Driven Trend-Aware Rule Mining for Dynamic Menu Bundling Andri Triyono; Kartika Imam Santoso; Rohman Hadi Al Haq
SMARTICS Journal Vol 12 No 1 (2026): Journal SMARTICS (April 2026)
Publisher : Universitas PGRI Kanjuruhan Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21067/smartics.v12i1.14079

Abstract

Digital transformation in the culinary industry currently demands moving beyond writing static lines of code, instead acting as an AI orchestrator adaptive to real-world conditions. This research focuses on addressing significant challenges in traditional data mining methods, such as the Apriori and FP-Growth algorithms, which often lack the flexibility to handle dynamic variables like ambient temperature fluctuations.Through the innovative orchestration of the Trend-Aware Rule Mining (TARM) algorithm and a LangGraphbased Agentic Workflow, this study transforms raw association rules into strategic business decisions via an iterative reasoning process and self-correction mechanism. Experimental results on a dataset of 52,494 rows demonstrate TARM's computational superiority, with memory usage of only 8.04 MB , significantly more efficient than Apriori's 127.44 MB. Furthermore, the synergy between the Strategy Agent and Evaluator Agent achieved a logic consistency score of 100% , validated by an independent audit with an average score of 96.25%.These findings confirm that the developed system is in a ready-to-use state to support precise and adaptive decision-making automation in production environments.
IMPLEMENTASI ALGORITMA FP-GROWTH UNTUK REKOMENDASI PRODUK DI TOKO LM MART Happy Dewi Ariyantini; Dhika Malita Puspita; Andri Triyono
Julia: Jurnal Ilmu Komputer An Nuur Vol 4 No 1 (2024): Julia Jurnal
Publisher : LPPM Universitas An Nuur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35720/julia.v4i1.4

Abstract

LM Mart merupakan salah satu usaha toko BumDesa yang berlokasi di Jl Raya PurwodadiSemarang Km.13 kecamatan Godong Kabupaten Grobogan. Produk yang dijual meliputi berbagai bahan pangan pokok (sembilan bahan pokok) untuk kebutuhan masyarakat umum. Data disimpan dalam database toko LM Mart. Salah satunya adalah memperbanyak data transaksi. Dengan semakin meningkatnya volume data di LM Mart, fungsi analis yang menganalisis data secara manual harus digantikan dengan aplikasi berbasis komputer. Permasalahan yang ada pada Toko LM Mart adalah pedagang kurang mempunyai kemampuan dalam mengamati keinginan dan kebutuhan konsumen yang tentunya akan berdampak pada peningkatan penjualan produk. Selain itu data transaksi penjualan jika diolah dapat menghasilkan informasi bermanfaat yang dapat menjadi strategi penjualan untuk meningkatkan pemasaran. Algoritma FP-Growth akan digunakan untuk pendekatan asosiasi pada penelitian ini. Algoritma FP-Growth merupakan pengembangan dari algoritma apriori, memperbaiki kekurangan dari algoritma apriori. Untuk mendapatkan kumpulan item yang sering, algoritma apriori harus menghasilkan kandidat. Dari hasil penelitian perhitungan menggunakan RapidMiner dengan nilai Support sebesar 30% dan nilai Confidance sebesar 80% dengan data transaksi sebanyak 800 record menghasilkan 36 rule. 
ANALISIS SENTIMEN PADA TWITTER TENTANG ISU PERILAKU ANTISOSIAL DENGAN ALGORITMA NAÏVE BAYES Retika Nur Fadila; Andri Triyono; Dhika Malita Puspita
Julia: Jurnal Ilmu Komputer An Nuur Vol 4 No 1 (2024): Julia Jurnal
Publisher : LPPM Universitas An Nuur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35720/julia.v4i1.5

Abstract

In 2023, around 78.19% of the 275.77% or 215.63 million Indonesian population will be connected to the internet, with positive impacts such as fast communication, entertainment and new knowledge. The internet makes non-cash transactions easier and has negative impacts such as addiction and antisocial behavior such as indifference to people around you. Teenagers often access social media, especially Twitter, to express opinions and vent both positive and negative. Sentiment analysis is used to determine opinions about antisocial behavior on Twitter by using text mining techniques to analyze teenagers' opinions. Naive Bayes and SVM algorithms are used in sentiment analysis on the Twitter dataset to analyze antisocial behavior. Actions to evaluate the Naive Bayes algorithm in assessing antisocial behavior sentiments had the best accuracy results of 59.71% with k=7 without n-grams. The Naïve Bayes algorithm with k=5 and n-gram n=2 has the best precision of 33.76% and the best recall of 33.45%. Future research can try to use other classification algorithms such as KNN, SVM, etc. To find the best accuracy of the antisocial behavior dataset. 
IMPLEMENTASI ALGORITMA APRIORI UNTUK MENCARI POLA TRANSAKSI PENJUALAN PADA TOKO PERTANIAN TOKO BIDSALTANI Muhamad Nuryahya; Andri Triyono; Agus Susilo Nugroho
Julia: Jurnal Ilmu Komputer An Nuur Vol 4 No 1 (2024): Julia Jurnal
Publisher : LPPM Universitas An Nuur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35720/julia.v4i1.20

Abstract

Progress in the industrial sector is currently growing rapidly, especially in medium and upper-class businesses, especially in agricultural shop businesses. Agricultural shops are one of the medium-sized businesses where competition is quite tight, this can be seen from the high consumer demand for fertilizer and agricultural equipment.With the high demand of consumers for agricultural needs as well as intense competition, agricultural shop companies must further improve their business performance in order to be able to face the problems that occur.Bidsal Tani is one of the many agricultural shops in Purwodadi District that sells agricultural necessities, such as chemical fertilizer, compost, plant seeds and all other agricultural necessities, it can be seen that to make a profit as expected.The a priori algorithm is a market basket analysis algorithm used to produce association rules. Association rules can be used to find relationships or cause and effect. The results of the research are that the products frequently purchased by consumers are PHONSKA, NPA, ZA, FASTAC, KOGE, UREA, GANDASIL, FLORAN, SP36, TSP, WUXAL, BAYFOLAN, BLOPATEK, KCL, HYDRASIL AND DECIS products.
TRANSFORMASI DIGITAL UMKM PERCETAKAN: OPTIMALISASI PLATFORM ECOMMERCE TERINTEGRASI PADA ESPRINT.STORE Muhammad Nabil Musyarof; Afif Kisnandhya Putra; Nibroos Naufal Islam; Rizky Dwi Astuti; Kartika Imam Santoso; Andri Triyono
Julia: Jurnal Ilmu Komputer An Nuur Vol 5 No 2 (2025): julia.ejournal.unan.ac.id
Publisher : LPPM Universitas An Nuur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35720/julia.v5i2.27

Abstract

Digital transformation has become a strategic necessity for Micro, Small, and Medium Enterprises (MSMEs), particularly in the printing sector which demands speed, flexibility, and personalized services. This study aims to examine the effectiveness of the esprint.store platform as a web-based eCommerce solution integrated with WhatsApp API and a dynamic pricing system. A mixed-method approach was employed, combining Google Analytics data, a System Usability Scale (SUS) questionnaire from 120 respondents, and system architecture observation. The results indicate a 35% increase in sales conversion and a reduction in customer response time from 24 hours to 15 minutes. These findings suggest that digitalization through a simple yet functional system can enhance service efficiency and customer satisfaction within the MSME.
Model Hibrid Keamanan Komunikasi Data Menggunakan Kriptografi Berbasis Federated Lattice Dan Stegnografi Homomorfik Dengan Optimasi Quantum-Resistant Protocol Yekti Kuncorojati; Metha Mudrifah Zain; Sindy Hertika Putri Sindy; Kartika Imam Santoso; Andri Triyono
Julia: Jurnal Ilmu Komputer An Nuur Vol 6 No 2 (2026): juliajurnal
Publisher : LPPM Universitas An Nuur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35720/julia.v6i2.53

Abstract

Data communication security faces significant challenges with the development of quantum computing and the increasing complexity of cyberattacks. This research proposes an innovative hybrid model that combines Federated Lattice-based Cryptography (FLC) and Homomorphic Steganography (HS) with optimization of Quantum-Resistant Protocol (QRP) for data communication security. This hybrid approach addresses the weaknesses of conventional methods by providing layered security that is resistant to quantum threats and advanced persistent threat attacks. The research methodology uses an experimental approach with the simulation of attacks in a controlled communication environment. Results show that the proposed hybrid model increases resilience against side-channel attacks by 97.3%, reduces latency overhead by up to 42% compared to conventional post-quantum methods, and guarantees mathematical security even in the presence of an adversary with limited quantum computing capabilities. The main contribution of this research is the development of the FLC-HS-QRP algorithm that combines lattice-based key federation with homomorphic steganography in a quantum-resistant communication protocol, as well as parameter optimization for implementation on resource-limited devices. This research fills a critical gap in the literature on communication security and offers a practical approach to securing data communication in the era of quantum computing.
Co-Authors Afif Kisnandhya Putra Agus Susilo Nugroho Arum, Dhika Malita Puspita Dhika Malita Puspita Happy Dewi Ariyantini Kartika Imam Santoso Metha Mudrifah Zain Muhamad Nuryahya Muhammad Nabil Musyarof Nibroos Naufal Islam Rahmawan Bagus Trianto Retika Nur Fadila Rizky Dwi Astuti Rohman Hadi Al Haq Sindy Hertika Putri Sindy Yekti Kuncorojati