Penggunaan Kata Cantik Dalam Majalah Wanita Femina, Kebaya Dan Kartini Edisi Oktober, November, Desember 2015 Dan Penerapannya Dalam Pelajaran Bahasa Di Sekolah

Magazine is one form of media board composed based on words, phrases, clauses, and sentences made by the authors to attract readers. He wrote the language did not heed the arrangement grammatical sentence. The purpose of this study identifies a form of words and the function words that exist in the women's magazine Femina, Kebaya and Kartini edition of October, November, December 2015, and the application of lessons syntax in language lessons in school about the form of words, word form, and function in school. Sources of data in this study is a women's magazine Femina, Kebaya and Kartini 2015. In non-interactive data collection researchers used see and record. This type of research is qualitative descriptive. Researchers analyzed using basic techniques pilah decisive element (PUP) is sifting through the data concerned with the referent or reference. This study shows that a form of words that exist in the women's magazine Femina, Kebaya and Kartini edition of October, November, December 2015 consists of two forms of full words and word tasks. Femina, Kebaya and Kartini 2015 the dominant form full words for words that define overall pretty words have meaning and can stand alone. As in the full word: white, fashionable, exclusive, glamur, graceful, slender, charming, tender, sexy, beauty, clean, smooth, elegant and casual. Said task: and, besides, nan, from, with, forms a loan word as promotional products. Function words syntactically in a women's magazine Femina, Kebaya and Kartini edition of October, November, December 2015 serves as the said subject, predicate, object and information. Beautiful words as fillers function objects in the magazine Femina, Kebaya and Kartini 2015 more dominant. Form of words that exist in the magazines category of objects in the words: white, pretty, slim, sweet, graceful, beauty, charming, alluring, classy, lovely While on the subject in general the word beautiful is a complement of the subject to show the nature and identity. Implementation in language learning in school to help for the sisiwa terutaman on the level / high school level to be able to know the function and form of the subject, predicate, object, along with the description of the type of words contained in the sentence / discourse femina women's magazine, kebaya, and Kartini, 2015

Analisis Faktor Yang Mempengaruhi Penerimaan Aplikasi Yantek Mobile Dengan Metode Technology Acceptance Model (TAM) dan Unified Theory Of Acceptance And Use Of Technology (UTAUT)

As technology develops, PT PLN (Persero) succeeded in changing the perspective and way of working of Technical Service officers (Yantek) by transforming a service culture that prioritizes the best, simple, fast, integrated, and real-time services. The presence of this digitalization program provides a challenge for Yantek officers who were previously unfamiliar with the use of digital technology. So it’s necessary research to measure the success and impact of the digital transformation that has been underway. This research was conducted to identify the factors that influence the acceptance of the digital transformation of the Yantek Mobile application, which was tested using Technology Acceptance Model (TAM) method and compared with Unified Theory of Acceptance and Use of Technology (UTAUT) method with data sampling on Yantek officers at PLN UID Kaltimra conducted with an online questionnaire survey. The test results show that two hypotheses are rejected, namely perceived usefulness (PU) and user attitude (ATU), which do not affect the user's interest in accepting the application. Factors that have a significant influence are dominated by external factors from users, such as social factors (SI) and supporting facilities (FC), so this can be a recommendation to PLN in improving future application development. Keywords: Digitalization, TAM, Transformation, UTAUT, Yantek Mobile.   ABSTRAK Seiring berkembangnya teknologi, PT PLN (Persero) berhasil mengubah cara pandang dan cara kerja petugas Pelayanan Teknik (Yantek) dengan transformasi budaya layanan yang mengedepankan pelayanan terbaik, sederhana, cepat, terintegrasi, dan real-time. Hadirnya program digitalisasi ini memberikan tantangan bagi petugas Yantek yang sebelumnya awam dalam penggunaan teknologi digital. Sehingga perlu dilakukan penelitian untuk mengukur keberhasilan dan dampak dari transformasi digital yang telah berjalan. Penelitian ini dilakukan untuk mengidentifikasi faktor-faktor yang berpengaruh dalam penerimaan transformasi digital aplikasi Yantek Mobile yang diuji dengan metode Technology Acceptance Model (TAM) dan dibandingkan dengan metode Unified Theory of Acceptance and Use of Technology (UTAUT) dengan data sampling pada petugas Yantek di PLN UID Kaltimra yang dilakukan dengan survey kuesioner daring. Hasil uji dan analisa diperoleh bahwa terdapat dua hipotesa yang ditolak yaitu perceived usefulness (PU) dan sikap pengguna (ATU) yang tidak memiliki pengaruh terhadap minat pengguna dalam menerima aplikasi. Faktor yang berpengaruh signifikan banyak didominasi oleh faktor eksternal dari pengguna seperti faktor sosial (SI) dan fasilitas pendukung (FC) sehingga hal tersebut dapat menjadi rekomendasi kepada PLN dalam peningkatan pengembangan aplikasi kedepannya. Kata Kunci: Digitalisasi, TAM, Transformasi, UTAUT, Yantek Mobile

Pemanfaatan Machine Learning untuk Pengelompokan dan Prediksi Target Tambah Daya Listrik Pelanggan Prabayar (Studi Kasus : PT PLN ULP Watang Sawitto)

Perkembangan teknologi sistem informasi dan ilmu pengetahuan khususnya dalam bidang pemasaran membuat para pelaku usaha berupaya untuk meningkatkan competitive advantage mereka dengan mengerahkan sumber daya yang dimiliki oleh perusahaan. Perusahaan dituntut untuk berinovasi dalam mengelola perusahaannya agar dapat bertahan dalam dunia persaingan. Kemampuan untuk memprediksi pelanggan prabayar yang berpotensi tambah daya listrik merupakan salah satu strategi pendukung untuk keberhasilan program pemasaran tambah daya pelanggan berdasarkan karakteristik konsumsi listriknya. Berdasarkan hal tersebut, penelitian ini mengajukan metode prediksi pelanggan prabayar dengan memanfaatkan algoritma pengelompokan (Clustering) dan klasifikasi. Data yang diolah adalah data pelanggan prabayar tarif rumah tangga yang memiliki fitur variabel daya listrik pelanggan (VA), frekuensi beli token listrik, total pemakaian kWh, total rupiah pembelian token, selisih daya VA pelanggan, jam nyala, periode hari pembelian token listrik, dan riwayat tambah daya listrik pelanggan. Pengelompokan dilakukan dengan menerapkan algoritma K-means. Dari hasil tersebut, model prediksi dibangun sesuai target setiap klaster dengan memanfaatkan dua metode, Gradient Boosting dan Artificial Neural Network. Evaluasi prediksi model terbaik dilakukan dengan menerapkan tiga skenario proporsi data latih dan data uji, yang selanjutnya diukur menggunakan matrik akurasi dan Cohen Kappa. Hasil eksperimen menghasilkan empat klaster berdasarkan karakteristik konsumsi listriknya. Gradient Boosting memberikan hasil yang terbaik untuk semua klaster, untuk klaster 1 menghasilkan nilai AUC 0.784, klaster 2 menghasilkan nilai AUC 0.941, klaster 3 menghasilkan nilai 0.884 dan klaster 4 menghasilkan nilai AUC 0.903

Rancang Bangun Sistem Informasi Manajemen Penanganan Kebencanaan

Indonesia merupakan salah satu negara yang secara geografis terletak di kawasan Ring of Fire atau Cincin Api Pasifik, dan karenanya Indonesia merupakan salah satu negara yang sering dilanda bencana alam seperti gempa bumi, letusan gunung berapi hingga tsunami. Untuk penanggulangan, pemerintah berupaya membangun berbagai fasilitas pelayanan publik seperti membentuk pos penyimpanan logistik kebutuhan, pos pengungsian, hunian sementara, masjid darurat dan beberapa fasilitas publik lainnya. Agar persebaran fasilitas pelayanan publik ini merata, pemerintah seringkali menempatkan tersebar di beberapa titik. Untuk penyesuaian kebutuhan setiap pos, disediakan catatan untuk mencatat data tentang pos yang sedang berjalan, seperti daftar penyintas, daftar relawan, daftar barang-barang logistik dan keuangan pos. Peningkatan efektifitas pencatatan menjadi landasan utama dalam tugas akhir ini dalam bentuk sebuah perangkat lunak yang digunakan untuk mencatat informasi penanganan kebencanaan. Hasil dari tugas ini adalah sebuah perangkat lunak yang secara efektif dapat mencatat segala keperluan penanganan kebencanaan dan kemudahan akses informasi bagi seluruh partisipan penanganan kebencanaan. Sejalan dengan peningkatan efektifitas pencatatan data, diharapkan dengan dibuatnya tugas akhir ini, tingkat pemerataan kebutuhan juga ikut meningkat dan dapat  mempercepat pemulihan pasca kebencanaan

ANALYSIS OF RAW MATERIAL INVENTORY PREDICTION FOR PLASTIC ORE USING A COMBINATION OF CAUSALITY AND TIME SERIES METHODS: A CASE STUDY IN A TEXTILE INDUSTRY COMPANY

Raw material inventory is a valuable company asset in production activities. Inadequate or excessive availability can lead to production failures or cost wastage. This research aims to predict raw material inventory based on factors such as initial stock, receipts, usage, final stock, and differences in usage. A causality-based approach with Multiple Linear Regression (MLR) is used as the basis, complemented by a time series data approach that processes data trends using the Bidirectional Long Short-Term Memory (BiLSTM) algorithm. The prediction results from both models are then combined using the harmonic mean. This research utilizes a dataset of raw material inventory and applies the Root Mean Squared Error (RMSE) and R-squared (R²) performance parameters for model evaluation. The research is expected to provide useful information for companies in managing their raw material inventory and improving the efficiency of their production processes. Results show that, in the BiLSTM deep learning model, Polyethylene Terephthalate (PET) raw materials yielded an RMSE of 6.53 and an R² of 0.93. These results indicate that PET raw materials have a higher predictive value than other materials

Identifikasi Fitur untuk Prediksi Penerimaan Program Listrik Prabayar: Kasus di PLN Tahuna

Listrik Prabayar (LPB) memberikan manfaat bagi perusahaan listrik dalam hal mengurangi piutang pelanggan dan memberikan kemudahan pengendalian pemakaian listrik bagi pelanggan. Perusahaan Listrik Negara (PLN) mempunyai program pemasaran untuk berpindah (migrasi) dari listrik pascabayar menjadi LPB. Pencapaian Key Performance Indicator (KPI) Program Pemasaran LPB PLN Tahuna pada 2021 hanya 1.185 pelanggan dari taget 2.261 pelanggan. Hal ini memberikan peluang perbaikan karena program pemasaran saat ini belum mengoptimalkan penggunaan data sebagai dasar penentuan prospek pelanggan. Penelitian ini mengajukan metode identifikasi fitur dan skenario pemilihan algoritma Pembelajaran Mesin yang tepat untuk memprediksi penerimaan pelanggan listrik pasca bayar terhadap program prabayar. Identifikasi fitur dilakukan dengan pengukuran korelasi Pearson. Kandidat algoritma Pembelajaran Mesin yang terpilih adalah Logistic Regression, Support Vector Machines, Decision Tree, dan Random Forest. Model-model yang dihasilkan dievaluasi menggunakan confusion matrix sehingga didapatkan model terbaik untuk studi kasus yang diajukan. Penelitian menunjukkan bahwa fitur tarif, daya, frekuensi terlambat membayar listrik, pemakaian rata-rata listrik bulanan (kWh) dan Kabupaten mempunyai korelasi signifikan dengan penerimaan LPB. Adapun model dengan algoritma Random Forest adalah model terbaik sesuai tujuan penelitian dengan F1-Measure tertinggi (95,17%)

LOAD FORECASTING FOR DAILY LOAD OPERATIONAL PLAN USING LSTM (CASE STUDY: SOUTH SULAWESI SUB SYSTEM)

The electrical load required in an electricity sub-system changes every day. Electric power operators must be able to generate and distribute electricity according to consumer needs. In the Sulawesi sub-system, the power plants used are still dominated by fossil fuel generators, so that in their operations, fuel requirements need to be given serious attention. Planning a good daily electricity consumption is needed so that the fuel cost becomes optimal. In the current condition, the load forecasting for the Daily Load Operation Plan (ROH) is still based on Expert Judgment, which is different for each forecaster. With a fairly large error tolerance limit of 4%. We need a load forecasting instrument capable of better error tolerance. Forecasting methods such as ARIMA, SARIMA and ARIMAX have been used for many years. In recent years, several artificial intelligence techniques such as Neural Network and machine learning have been developed for time series analysis. And recently, more accurate forecasting results are shown by Artificial Neural Network (ANN) and Recurrent Neural Network (RNN) compared to traditional forecasting methods. Long Short Term Memory (LSTM) is a model of RNN that uses past data (Long Term) to predict current data (Short Term). Electric load in Sulawesi subsystem used as data training after normalized using min-max normalization. The LSTM model is made with different data input. Forecasting  performance of each model is then evaluated based on the RMSE and MAPE values. Of the several data input models, forecasting models with daily data input show better performance than other scenarios. The MAPE and RMSE values obtained were 2.384% and 33.95, respectively

Prediksi Akumulasi Kasus Terkonfirmasi Covid-19 Di Indonesia Menggunakan Support Vector Regression

Indonesia merupakan salah satu negara di dunia yang terdampak parah oleh gelombang kedua COVID-19. Salah satu cara untuk meningkatkan kesadaran masyarakat terhadap wabah penyebaran virus adalah dengan memberikan informasi tentang prediksi kasus baru. Memprediksi akumulasi kasus dalam beberapa hari ke depan juga sangat penting untuk memperkirakan kebutuhan rumah sakit dan membantu pemerintah dalam membuat kebijakan. Di sisi lain, pola kasus gelombang kedua sulit untuk disimulasikan dengan pendekatan regresi tradisional. Penelitian ini berfokus pada pembuatan sistem informasi yang memberikan visualisasi prediksi akumulasi kasus COVID-19 di Indonesia dengan menggunakan Support Vector Regression (SVR). Algoritma pembelajaran ini dipilih karena kinerjanya yang sangat baik untuk menangani prediksi deret waktu. Hasil eksperimen menunjukkan bahwa SVR dapat memprediksi jumlah akumulasi kasus selama 30 hari ke depan dengan akurasi di atas 80%. Model prediksi tersebut kemudian dipasang pada aplikasi berbasis web, dan hasilnya divisualisasikan sesuai dengan data terbaru

DESIGN OF I-SLA (ISLAMIC LEARNING APPLICATION) AS TAJWEED LEARNING MEDIA BY USING THE SPEECH RECOGNITION TECHNOLOGY

Indonesia is a country with the majority of the population converting to Islam, which is more than 87% of the total population of Indonesia. As Muslims who adhere to the teachings of Islam, the teachings that must be understood are tajweed lessons. Tajweed science is the science that studies how to read the Qur'an properly and correctly. Adherents of Islam in Indonesia are still many who do not understand and cannot read the Qur'an properly and correctly. Research noted that there are still about 65% of Indonesian Muslims still blind to the writings of the Qur'an. The importance of learning tajweed science is that it can read precisely, if there are errors in reading the Quran can change its true meaning. Tajweed lessons are commonly obtained through non-formal educational institutions that focus on learning Islam. The current pandemic period causes all learning activities to be limited and difficult, including learning al quran education.  Online learning applications today are still rare that develop Quran Education including tajweed science, so people who want to learn the science have not found the right tools. We are planning an application called I-SLA (Islamic learning application). I-SLA is a tajweed learning application that utilizes speech recognition to correct the pronunciation of the user's Quran and provide justification if in pronunciation there is still something wrong, this technology has the ability to exchange information using acoustic signals. In addition, there is a consulting feature of tajweed experts if they feel they want to deepen tajweed knowledge. The design of the application in this study was carried out in a direct manner. The mechanism of this research is made by conducting a literature study for the process of making software needs specifications, followed by the creation of software design with UI / UX, followed by the creation of applications and closed with testing. This process is carried out continuously in accordance with the planning period. The result of this study is the application of I-SLA (Islamic Learning Application) with the aim of users of children, adolescents, and adults who want to deepen tajweed science to improve its pronunciation