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All Journal JURNAL SIMETRIK METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi Bhakti Persada Jurnal Aplikasi IPTEKS Just TI (Jurnal Sains Terapan Teknologi Informasi)
Ni Gusti Ayu Putu Harry Saptarini
Politeknik Negeri Bali
Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering

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PERENCANAAN SISTEM INFORMASI AUDIT MUTU INTERNAL DENGAN ZAHMAN FRAMEWORK STUDI KASUS POLITEKNIK NEGERI BALI I Komang Sugiarta; I Wayan Suasnawa; Ni Gusti Ayu Putu Harry Saptarini
JURNAL SIMETRIK Vol 9, No 1 (2019)
Publisher : Pusat Penelitian dan Pengabdian Kepada Masyarakat (P3M) Politeknik Negeri Ambon

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (450.095 KB) | DOI: 10.31959/js.v9i1.202

Abstract

Paradigma baru dalam sistem pendidikan memerlukan penyelenggaraan pendidikan yang akuntabel dan berkualitas. Sebagai upaya untuk mencapai hal ini, diperlukan evaluasi secara mandiri melalui sistem pengendalian internal oleh unit pengawas atau penjaminan mutu. Sistem penjaminan mutu internal bertugas sebagai auditor internal untuk membantu manajemen/pimpinan untuk melakukan evaluasi dalam rangka meningkatkan efektifitas manajemen resiko, pengendalian, dan tata kelola institusi di Politeknik Negeri Bali. Pengelolaan informasi merupakan unsur yang penting dari sistem penjaminan mutu internal. Dalam hal ini diperlukan sistem informasi untuk dapat membantu pelaksanaan audit mutu. Tujuan penelitian ini adalah untuk merencanakan sistem informasi untuk membantu pelaksanaan audit mutu internal. Perencanaan sistem informasi ini mengacu pada zahman framework dalam pemodelan sistem informasi. Pemodelan dengan zahman framework mendefinisikan manajemen keseluruhan yang diperlukan dengan melakukan analisis dan desain sistem informasi untuk menopang pelaksanaan audit oleh unit penjaminan mutu. Hasil yang didapatkan dari penelitian ini berupa blueprint rancangan sistem informasi audit mutu internal.
AJARINCODE : APLIKASI PEMBELAJARAN BAHASA PEMROGRAMAN BERBASIS WEB Ni Gusti Ayu Putu Harry Saptarini; Reza Akbar Hidayat; Putu Indah Ciptayani
Just TI (Jurnal Sains Terapan Teknologi Informasi) Vol 10, No 2 (2018): JULI 2018
Publisher : Politeknik Negeri Samarinda

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (283.772 KB) | DOI: 10.46964/justti.v10i2.106

Abstract

Bahasa pemrograman merupakan suatu subjek yang sangat populer saat ini. Pembelajaran bahasa pemrograman dapat dilakukan melalui pendidikan formal maupun informal seperti lembaga kursus atau pembelajaran secara online. Pembelajaran secara online memberikan berbagai keunggulan, diantaranya biaya yang lebih murah, waktu yang fleksibel dan setiap peserta dapat menyesuaikan dengan kemampuan dan perkembangan belajarnya. AjarinCode dibangun untuk membantu para pembelajar online dalam memahami bahasa pemrograman, khususnya bahasa pemrograman website yang meliputi PHP, HMTL, CSS, dan MySQL. Aplikasi ini akan memberikan materi sesuai dengan level pemahaman peserta, dan di setiap akhir pembelajaran akan diberikan ujian online dengan metode pengacakan soal menggunakan algoritma Fisher-Yates. Dengan algoritma ini, maka urutan soal yang dimunculkan akan selalu berbeda antara setiap peserta dan ketika seorang peserta mengulangi ujian. Peserta yang telah menyelesaikan ujian, nantinya akan dapat menempuh materi pada level lebih lanjut.
PENGEMBANGAN HILIRISASI PRODUK KOPI ARABIKA KINTAMANI Ni Gusti Ayu Putu Harry Saptarini; I Made Agus Putrayasa
Bhakti Persada Jurnal Aplikasi IPTEKS Vol 5 No 1 (2019): Mei 2019
Publisher : P3M Politeknik Negeri Bali

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (862.079 KB) | DOI: 10.31940/bp.v5i1.1076

Abstract

Kecamatan Kintamani merupakan sentra pengembangan Kopi Arabika yang ditunjukkan oleh adanya beberapa usaha mikro yang memproduksi kopi bubuk. Di antara pengusaha kopi yang sekaligus merupakan petani tersebut adalah I Ketut Jati dan I Dewa Ayu Putri Asih Banjar. Saat ini, kedua mitra tersebut menghadapi kendala dari sisi produksi, pemasaran, dan juga manajemen seperti kurangnya peralatan kemasan, sedikitnya pelanggan, serta tidak adanya manajemen keuangan yang baik. Usaha mereka memiliki kelemahan dalam menjaga mutu produksi sehingga kualitas kopi yang dihasilkan pun rendah. Dalam pemilihan biji kopi yang akan diolah, tidak ada standar kematangan yang diterapkan. Dalam hal ini, biji yang masih hijau sampai dengan biji yang mau jatuh pun juga dipakai. Selain itu, pemasaran yang dilakukan kedua mitra ini tidak efektif dan belum meluas. Teknik pemasaran dilakukan masih secara konvensional yaitu hanya dengan menjajakan produk di beberapa warung, dititipkan ke kolega yang kebetulan pameran, serta menunggu konsumen ataupun reseller yang datang. Hal ini tentunya menyebabkan penjualan produk yang tidak maksimal dan mengakibatkan keuntungan yang diperoleh kurang memuaskan. Berdasarkan analisis permasalahan di atas, maka dilakukan kegiatan sosialisasi, pemberian bantuan alat produksi, pelatihan manajemen keuangan, pembuatan website, pelatihan pengoperasian website, brosur serta pengajuan pembuatan surat Sertifikat Produksi Pangan Industri Rumah Tangga (SPP-IRT). Indikator capaian menunjukkan bahwa ada peningkatan produksi sebesar 100%, peningkatan jumlah pelanggan sebesar 50%, peningkatan penghasilan sebesar 100%, dan tercapainya manajemen keuangan mitra yang semakin baik. Kata kunci: kopi arabika kintamani, pemasaran online, manajemen keuangan Kintamani sub-district is a center for developing the Arabica Coffee indicated by the existence of several micro coffee industry entrepreneurs. Among those entrepreneurs who are basically farmers are I Ketut Jati and I Dewa Ayu Putri Asih Banjar. At present, these two partners face production, sales, and management obstacles such as lack of packaging equipment, lack of customers, and lack of good financial management. Their businesses have a weakness in maintaining their product quality so that the coffee quality is low. In selecting beans ripe which will be processed, they did not use a standard. In this case, the green coffee beans, as well as those that will be fallen, are also used. Furthermore, the marketing of the two partners is not effective and has not been widespread yet. Their marketing techniques are still carried out conventionally which are by peddling products in several stalls, entrusting sales to colleagues who went for an exhibition, and waiting for consumers or resellers to visit. This certainly leads to not optimal product sales and result in less satisfactory profits. Based on the aforementioned problem analysis, the socialization activities, provision of production tools, financial management training, website creation, website operation training, banners, stamps and brochures and the submission of a letter on the Household Food Production Certificate (SPP-IRT) were made. The indicators of achievement show that there is increased production by 100%, increased number of customers by 50%, increased income by 100%, and a better financial management that meets the standards can also be achieved. Keywords: kintamani arabica coffee, online marketing, financial management
Perancangan Sistem Peringatan Dini Bencana Berbasis Website Terintegrasi BMKG dan Notifikasi Whatsapp I Wayan Nuarta; I Putu Astya Prayudha; Ni Gusti Ayu Putu Harry Saptarini; Gde Brahupadhya Subiksa
METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi Vol. 10 No. 1 (2026): METHOMIKA: Jurnal Manajemen Informatika & Komputersisasi Akuntansi
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46880/jmika.Vol10No1.pp7-17

Abstract

Indonesia has a high level of disaster risk due to its geological conditions. Although the BMKG has provided early warning information, its dissemination has not been fully received by the public in a timely manner. Meanwhile, the widespread use of WhatsApp in Indonesia offers an opportunity to utilize it as a medium for delivering disaster-related information. This study aims to design a website-based disaster early warning system integrated with BMKG data and WhatsApp notifications based on provincial regions. The system was developed using the Waterfall method with PHP, MySQL, and a WhatsApp gateway service, utilizing earthquake and weather data that are updated automatically. The results indicate that the system operates according to functional requirements. WhatsApp notifications were successfully delivered with a 95% success rate and an average delivery time of 2.6 seconds. Black box testing showed 100% valid results, while the System Usability Scale evaluation achieved a score of 82.5, indicating a very good level of usability.
Evaluasi Model Convolutional Neural Network (CNN) dalam Klasifikasi Penyakit Daun Jagung Berbasis Web Menggunakan Citra Digital I Gusti Ngurah Arya Santika; Ni Gusti Ayu Putu Harry Saptarini; I Putu Astya Prayudha; Gde Brahupadhya Subiksa
METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi Vol. 10 No. 1 (2026): METHOMIKA: Jurnal Manajemen Informatika & Komputersisasi Akuntansi
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46880/jmika.Vol10No1.pp98-106

Abstract

Corn leaf diseases such as blight and rust can reduce crop yields if they are not detected at an early stage. In Penatahan Village, the process of identifying these diseases is still carried out manually through visual observation, which may lead to misidentification due to the similarity of symptoms between different diseases. Therefore, a technology-based system is needed to assist the identification process in a more objective and efficient manner. This study aims to classify corn leaf diseases using the Convolutional Neural Network (CNN) method based on digital leaf images. The dataset used consists of 319 images categorized into three classes: healthy, blight, and rust, with 80% of the data used for training and 20% for validation. The model was developed using a transfer learning approach with the MobileNetV2 architecture and evaluated using a confusion matrix. The experimental results indicate that the model achieved an accuracy of 92.19%, indicating that the CNN method is capable of effectively classifying corn leaf diseases. The developed system can be utilized as a tool to assist in the rapid and objective identification of corn leaf diseases.
Prediksi Kepatuhan Wajib Pajak PBB-P2 Berbasis Kecerdasan Buatan dengan Algoritma Gradient Boosting pada Badan Pendapatan Daerah Kabupaten Badung Caca Natasya Sugiana; I Putu Oka Wisnawa; Ni Gusti Ayu Putu Harry Saptarini
METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi Vol. 10 No. 1 (2026): METHOMIKA: Jurnal Manajemen Informatika & Komputersisasi Akuntansi
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46880/jmika.Vol10No1.pp234-243

Abstract

Rural and Urban Land and Building Tax (PBB-P2) is an important source of Regional Original Income (PAD). However, suboptimal taxpayer compliance often results in under-targeted regional tax revenues. Conventional taxpayer compliance management encourages the need for Artificial Intelligence (AI) and machine learning-based approaches to support more proactive, data-driven decision-making. This study uses the Extreme Gradient Boosting (XGBoost) algorithm to predict PBB-P2 taxpayer compliance based on historical tax data. The research steps include data preprocessing, splitting the training and testing datasets with a 70:30 ratio, model training, hyperparameter tuning, and model evaluation using precision, recall, F1-score, and confusion matrix. The results showed that the best model produced an F1-Macro value of 0.7108 with a learning rate of 0.2, max depth of 12, n_estimators of 400, min_child_weight of 1, subsample of 0.8, and gamma of 0. The most influential variables in the prediction included the District Code, Principal Payment Amount, and Village Code. The XGBoost model was able to provide quite good classification performance in supporting the identification of PBB-P2 taxpayer compliance more effectively.
Prediksi IHSG Berbasis Web Menggunakan Metode Long Short-Term Memory (LSTM) I Komang Agus Arta Cahyana; Ni Gusti Ayu Putu Harry Saptarini; Putu Desiana Wulaning Ayu
METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi Vol. 10 No. 1 (2026): METHOMIKA: Jurnal Manajemen Informatika & Komputersisasi Akuntansi
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46880/jmika.Vol10No1.pp249-259

Abstract

The Indonesia Composite Stock Price Index (IHSG) is one of the primary indicators used to measure the performance and stability of the capital market in Indonesia. The dynamic and non-linear characteristics of IHSG data make stock market prediction a challenging task when using conventional statistical methods. This study aims to develop a web-based IHSG prediction system using the Long Short-Term Memory (LSTM) method to improve prediction accuracy and provide an interactive forecasting platform for users. Historical IHSG data were collected from Yahoo Finance API using OHLCV (Open, High, Low, Close, Volume) variables. The data preprocessing stage included data cleaning, normalization using Min-Max Scaling, and sequence generation with the sliding window technique. Hyperparameter tuning was conducted by testing several configurations of window size, hidden units, learning rate, and epoch values. The best model configuration was obtained using a window size of 60, hidden units of 128, a learning rate of 0.01, and 74 epochs, resulting in RMSE of 51.9821, MAE of 39.8241, and MAPE of 0.559 %. Unlike previous studies that mainly focused on offline model evaluation, this research integrates the LSTM model into an interactive web-based prediction system equipped with visualization, AI forecasting, statistical evaluation, and batch prediction simulation features. The results indicate that the LSTM model is capable of producing accurate IHSG predictions and can be effectively implemented in a real-time web-based forecasting system.
Rekomendasi Kata Kunci Produk Variasi Motor Menggunakan Metode Analytical Hierarchy Process Hafidz Aby Pratama; Ni Gusti Ayu Putu Harry Saptarini; Ni Ketut Pradani Gayatri Sarja
METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi Vol. 10 No. 1 (2026): METHOMIKA: Jurnal Manajemen Informatika & Komputersisasi Akuntansi
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46880/jmika.Vol10No1.pp278-285

Abstract

The rising trend in motorcycle modification drives business owners to optimize digital marketing strategies through relevant product keywords. However, keyword determination is often conducted subjectively without a structured approach. This study aims to determine keyword recommendations for motorcycle variation products using the Analytical Hierarchy Process (AHP) method. The AHP method works by breaking down multi-criteria problems using pairwise comparison matrix calculations based on Relevance and Search Volume criteria. The results indicate that the AHP method is capable of determining keyword priorities objectively and systematically. The final results show that the keyword "Shockbreaker" ranks first with a score of 0,415. Consistency testing demonstrates a Consistency Ratio (CR) value of 0,00 for the criteria matrix and 0,081 for the search volume matrix. Since the CR values are CR ≤ 0,1, the weighting and decision-making results in this system successfully meet the standards established in the AHP method.
Implementasi XGBoost dan Logika Reverse Calculation pada Sistem Estimasi Harga Beli Mobil Bekas Berbasis Web Ida Bagus Aditya Cahya Wiraguna; Putu Indah Ciptayani; I Putu Bagus Arya Pradnyana; Ni Gusti Ayu Putu Harry Saptarini
METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi Vol. 10 No. 1 (2026): METHOMIKA: Jurnal Manajemen Informatika & Komputersisasi Akuntansi
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46880/jmika.Vol10No1.pp357-365

Abstract

The movement of used car prices is highly dynamic and subjective, making the manual estimation process prone to bias and financial risk. This study aims to develop a web-based used car purchase price estimation system using the Extreme Gradient Boosting (XGBoost) algorithm combined with a reverse calculation logic. The dataset was obtained from secondary market data and primary showroom transaction records, totaling 12,324 clean data after passing the Grouped-IQR outlier filtering process. The XGBoost model was optimized using Grid Search and validated through 10-Fold Cross-Validation. The results showed that the optimal model configuration achieved a Mean Absolute Percentage Error (MAPE) of 11.23%, a Root Mean Squared Error (RMSE) of Rp 54,779,437, and a Coefficient of Determination (R2) of 0.8386. This performance indicates a highly accurate forecasting capability. The model was successfully integrated into a Laravel-based web application via a Python REST API, allowing users to obtain fair market price predictions and maximum purchase bids to improve the efficiency and objectivity of decision-making.
Analisis Perbandingan Akurasi Metode Single Exponential Smoothing (SES) dengan Optimasi Parameter Alpha dan Single Moving Average Pada Sistem Peramalan Kebutuhan Stok Produk Bangunan: Studi Kasus: UD Ema Kencana Abadi I Made Sesa Putra; Ni Gusti Ayu Putu Harry Saptarini; Ni Nyoman Harini Puspita
METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi Vol. 10 No. 1 (2026): METHOMIKA: Jurnal Manajemen Informatika & Komputersisasi Akuntansi
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46880/jmika.Vol10No1.pp217-225

Abstract

Manual inventory management at UD Ema Kencana Abadi has the potential to cause recording errors and stock imbalances. The primary focus of this study is to implement a web-platform-based inventory system that integrates forecasting techniques as a decision-support instrument. The method used is Single Exponential Smoothing (SES) with dynamic optimization of the alpha (α) parameter through iteration to obtain the lowest Mean Absolute Percentage Error (MAPE), which is then compared with the Single Moving Average (SMA-3). Testing was conducted using nine months of historical sales data on six building material products. The results showed that SMA-3 is more optimal for data with stable fluctuations, yielding a MAPE of 11.91%, whereas the optimized SES is more accurate for data with medium to high fluctuations. The implementation of this system improves forecasting accuracy and supports more objective stock procurement decision-making.
Co-Authors Caca Natasya Sugiana Gde Brahupadhya Subiksa Hafidz Aby Pratama I Gusti Ngurah Arya Santika I Komang Agus Arta Cahyana I Komang Sugiarta I Made Agus Putrayasa I Made Sesa Putra I Putu Astya Prayudha I Putu Bagus Arya Pradnyana I Putu Oka Wisnawa I Wayan Nuarta I Wayan Suasnawa Ida Bagus Aditya Cahya Wiraguna Ni Ketut Pradani Gayatri Sarja Ni Nyoman Harini Puspita Putu Desiana Wulaning Ayu Putu Indah Ciptayani Reza Akbar Hidayat