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All Journal Jurnal Buana Informatika Jurnal Edukasi dan Penelitian Informatika (JEPIN) JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI Jutisi: Jurnal Ilmiah Teknik Informatika dan Sistem Informasi bit-Tech Informatics and Digital Expert (INDEX) Journal of Applied Computer Science and Technology (JACOST) MDP Student Conference
Jimmy Tjen
Universitas Widya Dharma Pontianak
Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering

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Journal : bit-Tech

 1 
Uncovering Legendary Coffee Shops in Pontianak Through Sentiment Analysis Ilucky Salim; Jimmy Tjen
bit-Tech Vol. 7 No. 3 (2025): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v7i3.2240

Abstract

Nowadays, coffee shops are scattered everywhere offering a variety of unique experiences to attract customers. Despite the rapid emergence of modern coffee shops, certain long-established coffee shops (often referred to as “legendary coffee shops”) continue to thrive and maintain a loyal customer base. The success of legendary coffee shops can be attributed to factors such as signature beverages, distinctive ambiance, and a strong word-of-mouth reputation. Unlike newer establishments that rely heavily on digital marketing, these coffee shops build trust and popularity over time. To further understand their influence, sentiment analysis can be applied to customer reviews of the coffee shops. This study analyzes two legendary coffee shops in Pontianak, namely Aming Coffee Shop and Asiang Coffee Shop to understand the key factors behind their sustainability despite strong competition using Naïve Bayes Method. The best accuracy for testing data at a 50:50 ratio was 76.76%, while training data reached 96.16%. The resulting precision and recall values are 96.16% and 78.81%. This study employs N-gram 3 model to identify the top words of both coffee shops. The findings indicates that both coffee shops are well-known for their signature milk coffee and unique flavor beverages that resonate with the local community. Aming Coffee Shop attracts young customers with affordable prices, while Asiang Coffee Shop maintains its traditional coffee shop ambiance, appealing to customers seeking nostalgia. From these two case studies, it is evident the success of a coffee shop is highly influenced by taste, branding, and customer experience.
Prediksi Safety Stock Produk Filter Oli Sepeda Motor Berbasis Demand Response (DR) - ARMA Sandi Tendean; Jimmy Tjen; Riyadi Jimmy Iskandar
bit-Tech Vol. 8 No. 1 (2025): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i1.2282

Abstract

Manajemen rantai pasokan merupakan hal krusial yang dibutuhkan dalam menjaga persediaan suatu produk supaya tetap tersedia selama masa tunggu. Hal ini bertujuan untuk menjaga keberlanjutan suatu bisnis sehingga penjualan produk tersebut tidak terganggu dengan permasalahan kurangnya persediaan. Namun, metode prediksi konvensional seperti ARMA-klasik dan ARMA-GARCH seringkali kurang akurat pada data riil yang bersifat sparse yang didominasi nilai nol dan fluktuatif. Penelitian ini bertujuan untuk menggagas sebuah metode Auto Regressive Moving Average (ARMA) baru yang menggabungkan konsep demand response dengan analisis galat yang bernama Demand Response-ARMA (DR-ARMA). Metode ini dikembangkan melalui tiga tahap, yaitu penurunan matematis berbasis RMSE dan analisis tren, adaptasi model untuk data sparse, dan validasi menggunakan data primer penjualan sparepart filter oli dari CV di Kalimantan Barat selama 60 hari. DR-ARMA mengoptimasi prediksi ARMA berdasarkan pada tren penjualan serta mengontrol ketidakpastian prediksi dengan memanfaatkan analisis galat, supaya kesalahan prediksi dapat berkurang selama perhitungan safety stock. Simulasi numerik dilakukan pada data penjualan filter oli dari sebuah perusahaan yang ada di Kalimantan Barat. Hasil simulasi menunjukkan bahwa metode DR-ARMA dapat memprediksi penjualan filter oli dengan akurasi 80%, lebih tinggi dibandingkan metode prediksi lainnya seperti ARMA-Generalized Auto Regressive Conditional Heteroskedasticity (GARCH) (74%) dan ARMA-klasik (57%). Metode DR-ARMA juga dapat digunakan untuk memprediksikan safety stock untuk 60 hari kedepan dengan tingkat kesalahan prediksi sekitar 17%. Hal ini menunjukkan bahwa metode DR-ARMA cocok digunakan untuk memprediksikan safety stock dari data yang bersifat sparse. Metode DR-ARMA dapat membantu pengguna dalam mengatur jumlah persediaan barang yang dibutuhkan tanpa perlu melakukan pengisian gudang secara berlebihan.
Safety Stock Forecasting using ARMA and DR-ARMA under Different Sparsity Levels Jasmine Putri Halim; Jimmy Tjen; Alvin Lesmana
bit-Tech Vol. 8 No. 2 (2025): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i2.3154

Abstract

Accurate demand forecasting is vital in supply chain management, particularly in the fast-moving consumer goods (FMCG) industry that experiences rapid stock turnover and fluctuating demand. The Auto-Regressive Moving Average (ARMA) has been the standard approach for time series forecasting, however it often underperforms under sparse and fluctuating data. This study contributes to the literature by applying Demand Response-ARMA (DR-ARMA) that was initially developed to address data sparsity and fluctuations under more complete and lower-sparsity data conditions. Using three primary datasets with varying sparsity levels from an FMCG distributor of bottled water products in West Borneo, DR-ARMA was benchmarked against classical ARMA. The results show that DR-ARMA consistently outperforms the classical ARMA model even under more complete, lower sparse data conditions. In lower sparsity datasets, DR-ARMA achieved average Mean of Percentage Error (MAPE) values of 22.64% and 6.41% respectively compared to the baseline ARMA model (235.60% and 180.86%). However, its best performance was observed in higher sparse condition (70.45%), achieving an average MAPE value of 1.79% across all datasets, suggesting the model remains most effective when applied to sparse data as originally intended. These improvement enables more precise safety stock planning, lower holding costs, and position DR-ARMA as a practical forecasting tool that connects analytical performance with real operational impact.
An Optimized Demand Response-ARMA Model for Inventory Management Under Intermittent Product Demand Vedano Gustine; Sandi Tendean; Jimmy Tjen
bit-Tech Vol. 8 No. 2 (2025): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i2.3326

Abstract

Forecasting accuracy serves a crucial role in supply chain management, especially in calculating safety stock and purchase limit, particularly under demand fluctuations such as those observed for air filter products, which are characterized as slow moving and intermittent. The DR-ARMA method was designed to model sparse data effectively, despite the model heavily relies on manual tuning factor selection. In this case, the model still face limitation in handling intermittent demand. To address such methodological gap, this study proposes an optimized version of the Demand Response-ARMA (DR-ARMA) model which is able to handle intermittent demand, named Optimized Demand Response-ARMA (ODR-ARMA) by applying optimization problems that lead to an adaptive error multiplier factor. Using air filter sales data with a sparsity level of 62.3% and a varying lead time assumption from a company located in Pontianak. The comparative analysis of ODR-ARMA against LSTM, GBRT, and DR-ARMA reveals that the ODR-ARMA model demonstrates the best performance for both safety stock and purchase limit calculations with an average accuracy of 81.11% and 96.09%, respectively. The optimization results in a significant improvement, as the DR-ARMA model achieves an average accuracy of 51.26% for safety stock calculation and 30.48% for purchase limit calculation. As the ODR-ARMA model has the capability to generate an accurate demand forecast and requires low computational resources, this model can be used as a basis for enterprises, especially SMEs in decision making related to inventory management, which allows enterprises to avoid the risks of stockout, excess stock, and dead stock.
Optimizing Raw Material Inventory for Culinary MSMEs under Data Scarcity: A DR-ARMA Forecasting Approach Venicia Lauren; Thommy Willay; Jimmy Tjen
bit-Tech Vol. 8 No. 3 (2026): bit-Tech - IN PROGRESS
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i3.3689

Abstract

Culinary MSMEs struggle with inventory management because raw materials perish quickly and daily demand fluctuates unpredictably. Most forecasting tools require extensive historical data, often unavailable in kitchens with sparse, intermittent sales records. To address this gap, this study develops and validates a Demand Response Auto-Regressive Moving Average (DR-ARMA) model that performs reliably under severe data constraints. DR-ARMA extends classical ARMA through three stages: baseline ARIMA modeling, moving-average trend detection, and adaptive calibration that incorporates forecast errors directly into safety stock computation via an RMSE-buffered adjustment. This mechanism treats safety stock as endogenous to the forecasting workflow rather than a post hoc decision, representing the core methodological innovation. The model simultaneously enhances forecast accuracy and safety stock reliability. We validated DR-ARMA using a three-month daily sales dataset from an Indonesian culinary business, comprising 90 observations, with over 30% of days with zero sales. Results demonstrate that DR-ARMA achieves a Mean Absolute Percentage Error of 24.64%, substantially outperforming Simple Moving Average (42.70%) and marginally improving upon the Naïve benchmark (24.99%). In this zero-inflated context, even modest gains in forecast stability directly reduce spoilage and stockouts. The integrated safety stock buffer provides an empirical service level of 80%, with tighter inventory bounds that prioritize waste reduction. Finally, we embedded the model into a desktop system, converting predictions into daily procurement lists. This study confirms DR-ARMA as a practical, theoretically grounded solution for inventory optimization in data-scarce culinary settings.
Co-Authors Alvin Lesmana Genrawan Hoendarto Ilucky Salim Jasmine Putri Halim Riyadi Jimmy Iskandar Sabila, Qolbi Sin Sandi Tendean Therence, Claretty Tony Darmanto Valentina, Cecilia Valentino Pratama Vedano Gustine Venicia Lauren Verita, Nengsy Willay, Thommy