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Statistical Machine Translation Pada Bahasa Lampung Dialek Api Ke Bahasa Indonesia Permata, Permata; Abidin, Zaenal
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 4, No 3 (2020): Juli 2020
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v4i3.2116

Abstract

In this research, automatic translation of the Lampung dialect into Indonesian was carried out using the statistical machine translation (SMT) approach. Translation of the Lampung language to Indonesian can be done by using a dictionary. Another alternative is to use the Lampung parallel body corpus and its translation in Indonesian with the SMT approach. The SMT approach is carried out in several phases. Starting from the pre-processing phase which is the initial stage to prepare a parallel corpus. Then proceed with the training phase, namely the parallel corpus processing phase to obtain a language model and translation model. Then the testing phase, and ends with the evaluation phase. SMT testing uses 25 single sentences without out-of-vocabulary (OOV), 25 single sentences with OOV, 25 compound sentences without OOV and 25 compound sentences with OOV. The results of testing the translation of Lampung sentences into Indonesian shows the accuracy of the Bilingual Evaluation Undestudy (BLEU) obtained is 77.07% in 25 single sentences without out-of-vocabulary (OOV), 72.29% in 25 single sentences with OOV, 79.84% at 25 compound sentences without OOV and 80.84% at 25 compound sentences with OOV.
DETERMINAN TERHADAP KUALITAS PELAPORAN KEUANGAN DAERAH Badjuri, Achmad; Jaeni, Jaeni; Sunarto, Sunarto; Permata, Permata; Yuditiyani, Yuditiyani
Proceeding SENDI_U 2020: SEMINAR NASIONAL MULTI DISIPLIN ILMU DAN CALL FOR PAPERS
Publisher : Proceeding SENDI_U

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Penelitian ini menguji dan menganalisis variabel independen yang terdiri dari pengaruh sumber dayamanusia, diterapkannya SAKD, teknologi informasi, sistem pengendalian internal dan standar akuntansipemerintah terhadap kualitas pelaporan keuangan daerah. Analisis yang digunakan adalah regresi linierberganda. Metode mengumpulkan data dengan menyebarkan kuesioner menggunakan skala likert 1-5. Objekdalam penelitian merupakan aparatur sipil negara (ASN) BPKAD Kota Semarang. Output penelitian ini dapatdisimpulkan bahwa semua variabel independen memiliki pengaruh yang positif dan signifikan terhadap kualitaspelaporan keuangan daerah.
Mendorong Produktivitas Kerja Melalui Peran Pelatihan dan Kepuasan Kerja Dengan Komitmen Organisaional Sebagai Intervening Penelitian Pada Karyawan Industri Genteng di Kebumen permata, permata; Harry Cahyono; Julinha Betty Guterres Riu
Sains Manajemen: Jurnal Manajemen Unsera Vol. 9 No. 2 (2023): Sains Manajemen: Jurnal Manajemen Unsera
Publisher : Universitas Serang Raya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30656/sm.v9i2.7747

Abstract

This study aims to investigate the effect of job training and job satisfaction on work productivity, with organizational commitment as an intervening variable. This research is a quantitative research with employee respondents in the tile industry in Kebumen Regency. Data is obtained with questionnaire distribution instruments, which then the collected data is processed with the help of AMOS software and Structural Equation Modeling (SEM) analysis tools. The results of the analysis show that the level of job satisfaction has a significant positive influence on employee work productivity, with organizational commitment acting as an intervening variable.On the other hand, research findings show that job training does not have a significant impact on organizational commitment or employee work productivity. This suggests that, although job training can improve employees' skills and knowledge, other factors may be more dominant in influencing organizational commitment and work productivity in the context of the tile industry in Kebumen District. The conclusion of this study highlights the importance of job satisfaction management as a strategy to increase employee work productivity. With the increase in job satisfaction, it can be expected that organizational commitment also increases, thus having a positive impact on work productivity.
Kombinasi Metode Analytical Hierarchy Process (AHP) dengan Metode Weighted Product (WP) pada Sistem Pendukung Keputusan Pemilihan Rumah Ideal Wantoro, Agus; Lutfy, Azza’zunda Choibar; Permata, Permata; Priandika, Adhie Thyo; Aryani, Venty
Jurnal Pendidikan dan Teknologi Indonesia Vol 4 No 9 (2024): JPTI - September 2024
Publisher : CV Infinite Corporation

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jpti.485

Abstract

Pemilihan rumah ideal merupakan keputusan penting yang memerlukan pertimbangan berbagai aspek untuk memastikan pilihan yang optimal. Rumah ideal biasanya dinilai dari beberapa kriteria utama, seperti harga, luas tanah, luas bangunan, jumlah kamar tidur, dan jarak lokasi dari pusat aktivitas. Namun, proses pemilihan rumah ideal sering menghadapi tantangan, seperti kesulitan dalam membandingkan berbagai alternatif yang memiliki berbagai kriteria dengan bobot yang berbeda. Penelitian ini bertujuan mengatasi permasalahan tersebut dengan menggunakan pendekatan metode sistem pendukung keputusan yaitu Pembobotan Matriks Berpasangan dari metode AHP dan Weighted Product (WP). Metode AHP digunakan untuk menentukan bobot relatif dari setiap kriteria berdasarkan penilaian dan perbandingan berpasangan. Metode WP digunakan untuk menghitung dan membandingkan alternatif berdasarkan bobot yang telah ditentukan. Data yang digunakan diambil dari situs web properti www.rumah123.com, yang mencakup informasi tentang (a) harga, (b) luas tanah, (b) luas bangunan, (c) jumlah kamar tidur, dan (d) jarak lokasi rumah di Bandar Lampung. Berdasarkan hasil analisis perhitungan menggunakan kombinasi metode Analytic Hierarchy Process (AHP), dan Weighted Product (WP) didapatkan nilai total untuk masing-masing alternatif yaitu (a) Mahkota Cluster 2 sebesar 0,2077, (b) Budaya Residence sebesar 0,2074, (c) Griya Anzana 3 sebesar 0,1968, (d) Raih Persada Residence sebesar 0,1960, (e) Ar-Rahman Residence sebesar 0,1921, (f) New Cordy Residences sebesar 0,1806, dan (g) The Rose Mansion sebesar 0,1737. Hasil perangkingan didapatkan Mahkota Cluster 2 merupakan alternatif rumah ideal terbaik di Bandar Lampung. Alternatif ini unggul dalam beberapa kriteria penting seperti jumlah kamar tidur, luas bangunan, serta harga yang kompetitif, meskipun jaraknya tidak yang terdekat dari pusat aktivitas. Penelitian ini memberikan informasi berupa rekomendasi bagi masyarakat yang ingin memilih rumah ideal agar tidak salah mengambil keputusan.
Development of a Decision Support System Based on New Approach Respond to Criteria Weighting Method and Grey Relational Analysis: Case Study of Employee Recruitment Selection Megawaty, Dyah Ayu; Damayanti, Damayanti; Sumanto, Sumanto; Permata, Permata; Setiawan, Dandi; Setiawansyah, Setiawansyah
JOIV : International Journal on Informatics Visualization Vol 9, No 1 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.1.2744

Abstract

The purpose of this research is to propose a new approach in the criteria weighting method using the RECA method, the RECA method can help provide a systematic and structured framework for determining criteria weights in multi-criteria decision making. The determination of weights using the RECA method is to increase objectivity and accuracy in the candidate assessment and selection process by determining the appropriate weight for each criterion based on responses and assessments from experts or stakeholders. Testing the RECA Method with Multi Attribute Decision Making (MADM) techniques is an important step in measuring the effectiveness of the RECA Method in the context of multi-criteria decision making. Ranking tests using Spearman correlation between the RECA method and other methods such as SAW with a correlation value of 1, MOORA with a correlation value of 0.9636, MAUT with a correlation value of 0.9515, WP with a correlation value of 0.891, SMART with a correlation value of 0.9636, and TOPSIS with a correlation value of 0.8788 show a high level of rank consistency between the RECA method and these methods. This indicates that the RECA Method has a strong ability to generate similar candidate rankings with other methods, validating its reliability and consistency in the context of multi-criteria decision making. Implications for further research include exploring the application of the RECA method in different decision-making contexts other than recruitment, such as performance evaluation, project selection, or supplier selection. Further research could investigate the integration of the RECA method with other decision-making methods or algorithms to improve its performance and applicability in complex decision environments. Comparative studies with larger sample sizes and diverse datasets can provide deeper insights into the effectiveness and reliability of the RECA method compared to other methods.
RAM-MEREC (Root Assessment Method - Method based on Removal Effects of Criteria): A Synergistic Approach to Weight Derivationand Alternative Ranking in the Selection of the Best Intern Employees Permata, Permata; Wang, Junhai; Setiawansyah, Setiawansyah; Pasaribu, A. Ferico Octaviansyah; Wahyudi, Agung Deni
TIN: Terapan Informatika Nusantara Vol 5 No 11 (2025): April 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v5i11.7198

Abstract

An effective intern selection process requires an objective and systematic approach to decision-making, especially when it involves multiple assessment criteria. This study proposes a combined approach of RAM-MEREC, which is a combination of Method based on Removal Effects of Criteria (MEREC) and Root Assessment Method (RAM), as a method to improve accuracy and reliability in the best internal selection. MEREC is used to objectively determine the weight of criteria based on the impact of the elimination of each criterion on the overall outcome. Meanwhile, RAM is used to generate alternative rankings by considering the root impact of value changes on each candidate's performance. The results of the application of this method show that RAM-MEREC is able to provide a more representative weighting and a more stable and consistent final ranking. The results of the application of this method show that RAM-MEREC is able to provide a more representative weighting and a more stable and consistent final ranking. The results of the calculation of the total score of all alternatives using the evaluation method that has been determined, obtained that Alternative 10 is the best candidate with the highest score of 1.4378, followed by Alternative 6 with a score of 1.4375 and Alternative 3 with a score of 1.4375. This approach not only improves the quality of decision-making, but also minimizes subjectivity and bias in the selection process.
Perbandingan Random Forest dan XGBoost Untuk Prediksi Penjualan Produk E-Commerce Rumah Madu Hayatunnisa, Destaria; Permata, Permata; Priandika, Adhie Thyo; Gunawan, Rakhmat Dedi
Building of Informatics, Technology and Science (BITS) Vol 7 No 3 (2025): December 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i3.8491

Abstract

Inventory management is one of the main challenges for small and medium enterprises (SMEs), including Rumah Madu in Bandar Lampung, where honey stock levels are often determined based on estimation rather than precise calculation. This study aims to analyze and compare the performance of the Random Forest and XGBoost algorithms in predicting honey sales to achieve more measurable stock management. The dataset consists of 1,699 honey sales transactions that have undergone cleaning, feature transformation, and standardization processes. The variables used include honey type, unit price, day, month, holiday status, and promotion indicators. Modeling was conducted using a time-series split approach, where historical data served as the training set and recent data as the testing set. The evaluation results show that Random Forest achieved an MAE of 24.35, RMSE of 29.04, and R² of -0.9685, while XGBoost achieved an MAE of 25.50, RMSE of 30.58, and R² of -1.1825. The negative R² values indicate that both models were unable to explain data variation optimally, with performance falling below a simple baseline. Nevertheless, the feature importance analysis revealed that unit price and honey type were the dominant factors influencing sales. This study highlights the need for further model development through parameter optimization and improved data quality to enhance prediction accuracy.
Translation of the Lampung Language Text Dialect of Nyo into the Indonesian Language with DMT and SMT Approach Abidin, Zaenal; Permata, Permata; Ariyani, Farida
INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi Vol 5 No 1 (2021): February 2021
Publisher : Universitas Nusantara PGRI Kediri

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (829.675 KB) | DOI: 10.29407/intensif.v5i1.14670

Abstract

Research on the translation of Lampung language text dialect of Nyo into Indonesian is done with two approaches, namely Direct Machine Translation (DMT) and Statistical Machine Translation (SMT). This research experiment was conducted as a preliminary effort in helping students immigrants in the province of Lampung, translating the Lampung language dialect of Nyo through prototypes or models was built. In the DMT approach, the dictionary is used as the primary tool. In contrast, in SMT, the parallel corpus of Lampung Nyo and Indonesian language is used to make language models and translation models using Moses Decoder. The result of text translation accuracy with the DMT approach is 39.32%, and for the SMT approach is 59.85%. Both approaches use Bilingual Evaluation Understudy (BLEU) assessment.
IMPLEMENTASI FORECASTING PADA PERENCANAAN SISTEM PEMESANAN BUKU LKS (LEMBAR KERJA SISWA) MENGGUNAKAN ALGORITMA REGRESI LINEAR. (STUDI KASUS: TOKO BUKU DARUL ULUM, PUNGGUR, LAMPUNG TENGAH permata, Permata
Jurnal Data Mining dan Sistem Informasi Vol 3, No 2 (2022): Agustus 2022
Publisher : Universitas Teknokrat Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33365/jdmsi.v3i2.2162

Abstract

Darul Ulum Bookstore is engaged in distributing LKS books to be sent to schools. The need for worksheets that support learning is one of the most important aspects of availability in the store. so it takes sufficient stock in the order at the beginning of the semester. In this case, the shop owner has difficulty in estimating the number of books to be ordered, so a calculation model is needed to estimate how many books will be ordered at the beginning of the semester. The Multiple Linear Regression method is one of the methods used to predict how many books will be ordered. This method uses the dependent variable and the independent variable as the basis by taking into account the initial stock of books for 2018 and 2019 as the independent variable (x) and the initial stock of 2020 as the dependent variable (y). The results of this study obtained a predictive accuracy value from each printing, namely for CV. Hasan Pratama with MAPE testing of 6.42% with very good indicators. CV. Pratama Mitra Aksara with MAPE testing of 23.52% the results of the indicators are feasible, and CV. Pilar Pustaka with MAPE testing of 6.75% the indicator results are very good. And visualization of predictive data using R-Markdown. Keywords: Linear Regression, Predicting, Interactive Website, R-Markdown
Perbandingan Kinerja Model ARIMA dan LSTM dalam Peramalan Harga Crypto Solana (SOL-USD) Berbasis Data Yahoo Finance Wadiyan, Wadiyan; Permata, Permata; Priandika, Adhie Thyo; Gunawan, Rakhmat Dedi
Building of Informatics, Technology and Science (BITS) Vol 7 No 4 (2026): March 2026
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i4.9444

Abstract

The extreme volatility and non-linear patterns of Solana (SOL) data, driven by its unique consensus mechanism and massive transaction volume, demand accurate forecasting methods to mitigate investment risks. This study compares the statistical method Autoregressive Integrated Moving Average (ARIMA) and Deep Learning Long Short-Term Memory (LSTM) using daily closing price data of SOL-USD from April 2020 to March 2025 obtained from Yahoo Finance. The ARIMA model was developed with optimal parameters (0,1,0), while the LSTM architecture utilized 50 hidden layer units with a 60-day timestep. Evaluation results indicate that the LSTM model significantly outperforms ARIMA, achieving an RMSE of 13.1352 and a MAPE of 6.07% (classified as highly accurate), compared to ARIMA's RMSE of 31.1241 and MAPE of 14.03%. The study concludes that neural network approaches are more effective and adaptive than traditional statistical methods in capturing the highly volatile price dynamics of crypto assets.