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POS Tagging Bahasa Madura dengan Menggunakan Algoritma Brill Tagger Nindian Puspa Dewi; Ubaidi Ubaidi
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 7 No 6: Desember 2020
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.2020722449

Bahasa Madura adalah bahasa daerah yang selain digunakan di Pulau Madura juga digunakan di daerah lainnya seperti di kota Jember, Pasuruan, dan Probolinggo. Sebagai bahasa daerah, Bahasa Madura mulai banyak ditinggalkan khususnya di kalangan anak muda. Beberapa penyebabnya adalah adanya rasa gengsi dan tingkat kesulitan untuk mempelajari Bahasa Madura yang memiliki ragam dialek dan tingkat bahasa. Berkurangnya penggunaan Bahasa Madura dapat mengakibatkan punahnya Bahasa Madura sebagai salah satu bahasa daerah yang ada di Indonesia. Oleh karena itu, perlu adanya usaha untuk mempertahankan dan memelihara Bahasa Madura. Salah satunya adalah dengan melakukan penelitian tentang Bahasa Madura dalam bidang Natural Language Processing sehingga kedepannya pembelajaran tentang Bahasa Madura dapat dilakukan melalui media digital. Part Of Speech (POS) Tagging adalah dasar penelitian text processing, sehingga perlu untuk dibuat aplikasi POS Tagging Bahasa Madura untuk digunakan pada penelitian Natural Languange Processing lainnya. Dalam penelitian ini, POS Tagging dibuat dengan menggunakan Algoritma Brill Tagger dengan menggunakan corpus yang berisi 10.535 kata Bahasa Madura. POS Tagging dengan Brill Tagger dapat memberikan kelas kata yang sesuai pada kata dengan menggunakan aturan leksikal dan kontekstual. Brill Tagger merupakan algoritma dengan tingkat akurasi yang paling baik saat diterapkan dalam Bahasa Inggris, Bahasa Indonesia dan beberapa bahasa lainnya. Dari serangkaian percobaan dengan beberapa perubahan nilai threshold tanpa memperhatikan OOV (Out Of Vocabulary), menunjukkan rata-rata akurasi mencapai lebih dari 80% dengan akurasi tertinggi mencapai 86.67% dan untuk pengujian dengan memperhatikan OOV mencapai rata-rata akurasi 67.74%. Jadi dapat disimpulkan bahwa Brill Tagger dapat digunakan untuk Bahasa Madura dengan tingkat akurasi yang baik. Abstract Bahasa Madura is regional language which is not only used on Madura Island but is also used in other areas such as in several regions in Jember, Pasuruan, and Probolinggo. Today, Bahasa Madura began to be abandoned, especially among young people. One reason is sense of pride and also quite difficult to learn Bahasa Madura because it has a variety of dialects and language levels. The reduced use of Bahasa Madura can lead to the extinction of Bahasa Madura as one of the regional languages in Indonesia. Therefore, there needs to be an effort to maintain Madurese Language. One of them is by conducting research on Madurese Language in the field of Natural Language Processing so that in the future learning about Madurese can be done through digital media. Part of Speech (POS) Tagging is the basis of text processing research, so the Madura Language POS Tagging application needs to be made for use in other Natural Language Processing research. This study uses Brill Tagger by using a corpus containing 10,535 words. POS Tagging with Brill Tagger Algorithm can provide the appropriate word class to word using lexical and contextual rule. The reason for using Brill Tagger is because it is the algorithm that has the best accuracy when implemented in English, Indonesian and several other languages. The experimental results with Brill Tagger show that the average accuracy without OOV (Out Of Vocabulary) obtained is 86.6% with the highest accuracy of 86.94% and the average accuracy for OOV words reached 67.22%. So it can be concluded that the Brill Tagger Algorithm can also be used for Bahasa Madura with a good degree of accuracy.

Penerapan Hidden Markov Model (HMM) dan Mel-Frequency Cesptral Coefficients (MFCC) pada E-Learning Bahasa Madura untuk Anak Usia Dini Ubaidi Ubaidi; Nindian Puspa Dewi
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 7 No 6: Desember 2020
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.2020722477

Bahasa Madura is a regional language used in Madura island. This language has many variations of pronunciation and dialect that makes it not easy to learn, even by the local people especially children. There hasn’t been any interesting learning media to learn Bahasa Madura so far. In fact, a fun learning activity is needed to help children to enhance their ability in pronouncing animals’ names, numbers, fruits and things in Bahasa Madura. Thus, it’s considered important to create Bahasa Madura e-learning by implementing the recognition of voice patterns in order to make it easier for the children to learn Bahasa Madura which has several variations of pronunciation only for one single object. This Bahasa Madura e-learning application for young learners is used to introduce Bahasa Madura vocabularies by recognizing the voice pattern recordings which have been processed through MFCC technique as the extracted voice features and HMM as the learning techniques. The implementation of MFCC and HMM as the learning tool to introduce the pronunciation of regional language vocabularies especially Bahasa Madura has never been done before. Therefore, this research is expected to help the young learners to be able to pronounce Bahasa Madura vocabularies properly. In this study, a number of young learners’ voices were recorded and were set as the trial data. Only the proper voice data that were used—voice data that were considered to be pronounced correctly. The trial method was done through one-single model and multi-model. After doing several simultaneous trials, the result showed the accuracy level. The average accuracy level for one-single model system was 73% (with the highest accuracy reached 75%) and the average accuracy level for multi-model system was 80% (with the highest accuracy reached 81%).

Combination of Genetic Algorithm and Brill Tagger Algorithm for Part of Speech Tagging Bahasa Madura Nindian Puspa Dewi; Joan Santoso; Ubaidi Ubaidi; Eka Rahayu Setyaningsih
Proceeding of the Electrical Engineering Computer Science and Informatics Vol 7, No 2: EECSI 2020
Publisher : IAES Indonesia Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eecsi.v7.2034

Part of speech (POS) is commonly known as word types in a sentence such as verbs, adjectives, nouns, and so on. Part of Speech (POS) Tagging is a process of marking the word class or part of speech in every word in a sentence. Part of Speech Tagging has an important role to be used as a basis for research in Natural Language Processing. That is why research on Part of Speech Tagging for Bahasa Madura as an effort to preserve and develop the use of regional languages. In this research, POS Tagging is done using the Brill Tagger Algorithm which is combined with the Genetic Algorithm. Brill Tagger is a POS Tagging Algorithm that has the best level of accuracy when implemented in other languages. Genetic Algorithms used in the contextual learner process with consideration in previous studies can increase the speed of the training process so that it is more efficient. The results of this study are then compared with the results of the previous study so that we can find out suitable algorithms used for the development of text processing in Bahasa Madura. From a series of experiments, the average accuracy obtained by using Brill Tagger is 86.4% with the highest accuracy of 86.7%, while using GA Brill Tagger shows an average accuracy of 86.5% with the highest accuracy of 86.6%. Testing by observing OOV (Out of Vocabulary) achieves an average accuracy of 67.7% for Brill Taggers and 64.6% for GA Brill Taggers. Testing by considering multiple POS with Brill Tagger produces an average accuracy of 73.3% while testing using GA Brill Tagger produces an average accuracy of 90.9%. This shows that the accuracy with GA Brill Tagger is better than Brill Tagger, especially if considering multiple POS. This is because GA Brill Tagger can generate rules for handling the existence of multiple POS more than pure Brill Tagger.Part of speech (POS) is commonly known as word types in a sentence such as verbs, adjectives, nouns, and so on. Part of Speech (POS) Tagging is a process of marking the word class or part of speech in every word in a sentence. Part of Speech Tagging has an important role to be used as a basis for research in Natural Language Processing. That is why research on Part of Speech Tagging for Bahasa Madura as an effort to preserve and develop the use of regional languages. In this research, POS Tagging is done using the Brill Tagger Algorithm which is combined with the Genetic Algorithm. Brill Tagger is a POS Tagging Algorithm that has the best level of accuracy when implemented in other languages. Genetic Algorithms used in the contextual learner process with consideration in previous studies can increase the speed of the training process so that it is more efficient. The results of this study are then compared with the results of the previous study so that we can find out suitable algorithms used for the development of text processing in Bahasa Madura. From a series of experiments, the average accuracy obtained by using Brill Tagger is 86.4% with the highest accuracy of 86.7%, while using GA Brill Tagger shows an average accuracy of 86.5% with the highest accuracy of 86.6%. Testing by observing OOV (Out of Vocabulary) achieves an average accuracy of 67.7% for Brill Taggers and 64.6% for GA Brill Taggers. Testing by considering multiple POS with Brill Tagger produces an average accuracy of 73.3% while testing using GA Brill Tagger produces an average accuracy of 90.9%. This shows that the accuracy with GA Brill Tagger is better than Brill Tagger, especially if considering multiple POS. This is because GA Brill Tagger can generate rules for handling the existence of multiple POS more than pure Brill Tagger

Aplikasi Marketplace Batik Madura di Sentra Batik Pasar 17 Agustus Pamekasan Ubaidi Ubaidi; Nindian Puspa Dewi
(JurTI) Jurnal Teknologi Informasi Vol 4, No 2 (2020): DESEMBER 2020
Publisher : Universitas Asahan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36294/jurti.v4i2.1687

Abstract - August 17 Market is one of the largest traditional markets in Pamekasan. This market is also well known as a batik market, which is the center of Madura Batik sales. In this market, traffic jams often occur, making it difficult for batik buyers to visit the batik market, which is located in the middle of the market. For this reason, it is necessary to create a marketplace for selling Batik Madura on August 17 Market. This Batik Sales Marketplace application is designed with Extreme Programming. This Marketplace application is made web and mobile-based so it can be accessed anywhere and anytime without having to market. Different from the previous application, to make it easier for visitors/buyers, besides being equipped with a short message feature, this application is also equipped with a seller location plan. That way, visitors/buyers can easily find seller locations if they want to shop offline. This feature is helpful in efficiency when searching. With this application, it can expand the marketing and promotion of Batik Madura as one of the cultural treasures of Madura.Keywords - Marketplace, Market, Batik, Batik Madura, Pamekasan. Abstrak - Pasar 17 Agustus merupakan salah satu pasar tradisional terbesar di Pamekasan. Selain sebagai pasar tradisional pada umumnya, pasar ini juga terkenal sebagai pasar batik yang menjadi pusat penjualan Batik Madura. Di pasar ini sering terjadi kemacetan sehingga menyulitkan para pembeli batik untuk mengunjungi pasar batik yang lokasinya dibagian tengah pasar. Karena itulah, perlu dibuat sebuah marketplace penjualan Batik Madura di Pasar 17 Agustus Pamekasan. Aplikasi Marketplace Penjualan Batik ini dirancang dengan Metode Agile Software Development jenis Extreme Programming. Aplikasi Marketplace ini dibuat dengan berbasis web dan mobile sehingga dapat diakses dimana saja dan kapan tanpa harus mengunjungi Pasar 17 Agustus Pamekasan. Berbeda dari aplikasi sebelumnya, untuk memudahkan pengunjung/pembeli, aplikasi ini selain dilengkapi dengan fitur pesan singkat juga dilengkapi dengan denah lokasi pelapak. Dengan begitu, pengunjung/pembeli dapat dengan mudah menemukan lokasi pelapak jika ingin berbelanja secara offline. Fitur ini sangat membantu dalam efisiensi waktu saat melakukan pencarian. Dengan adanya aplikasi ini maka dapat memperluas pemasaran dan promosi Batik Madura sebagai salah satu kekayaan budaya Madura yang banyak diminati oleh masyarakat di luar Madura. Kata Kunci - Marketplace, Pasar, Batik, Batik Madura, Pamekasan.

Rancang Bangun Sistem Informasi Manajemen Inventori Berbasis Web dan Android Nindian Puspa Dewi; Ridho Abdi Fadlillah
(JurTI) Jurnal Teknologi Informasi Vol 5, No 1 (2021): JUNI 2021
Publisher : Universitas Asahan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36294/jurti.v5i1.1791

Abstract - In trading companies the use of applications aims to increase productivity, both in obtaining, processing, and using data for the benefit of the company. The Rutaka shop is a micro, small and medium enterprise that is engaged in the sale of building materials, electrical equipment, and households. Inventory recording is done manually and does not yet have an integrated system between sales and warehouse. Purchases of goods are recorded in a logbook which allows the record to be lost or damaged. The sales department must go directly to the warehouse to ensure that the goods to be sold are still available. The researcher applies the moving average forecasting method which is used to determine the number of items to be ordered or purchased. The data that will be used in the forecasting is data on goods going out in the warehouse for the last three months. The results show that the use of this application can increase the effectiveness of recording orders, returning goods, incoming and outgoing goods, knowing the stock of goods available in the warehouse.Keywords - android, inventory, moving average, forecasting, web. Abstrak - Pada perusahaan dagang penggunaan aplikasi bertujuan untuk meningkatkan produktivitas, baik dalam memperoleh, mengolah, dan menggunakan data untuk kepentingan perusahaan. Toko Rutaka merupakan usaha mikro kecil dan menengah yang bergerak dibidang penjualan bahan bangunan, alat-alat listrik, dan rumah tangga. Pencatatan persediaan barang dilakukan secara manual dan belum memiliki sistem yang terintegrasi antara bagian penjualan dan gudang. Pembelian barang dicatat pada buku catatan yang memungkinkan catatan hilang atau rusak. Bagian penjualan harus turun langsung ke gudang untuk memastikan barang yang akan dijual masih tersedia. Peneliti menerapkan metode peramalan moving average yang digunakan untuk mengetahui jumlah barang yang akan di pesan atau beli. Data yang akan digunakan dalam peramalan tersebut yaitu data barang keluar pada gudang selama tiga bulan terakhir. Hasil penelitian menunjukkan bahwa penggunaan aplikasi ini dapat meningkatkan efektivitas dalam pencatatan pemesanan barang, retur barang, barang keluar dan masuk, mengetahui stok barang yang tersedia di gudang. Kata Kunci - android, inventori, moving average, peramalan, web.

Lexical Rule and Lexicon Effect for Part of Speech Tagging Bahasa Madura Nindian Puspa Dewi; Ubaidi Ubaidi
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol 18 No 1 (2018)
Publisher : LPPM Universitas Bumigora

POS Tagging adalah dasar untuk pengembangan Text Processing suatu bahasa. Dalam penelitian ini kita meneliti pengaruh penggunaan lexicon dan perubahan morfologi kata dalam penentuan tagset yang tepat untuk suatu kata. Aturan dengan pendekatan morfologi kata seperti awalan, akhiran, dan sisipan biasa disebut sebagai lexical rule. Penelitian ini menerapkan lexical rule hasil learner dengan menggunakan algoritma Brill Tagger. Bahasa Madura adalah bahasa daerah yang digunakan di Pulau Madura dan beberapa pulau lainnya di Jawa Timur. Objek penelitian ini menggunakan Bahasa Madura yang memiliki banyak sekali variasi afiksasi dibandingkan dengan Bahasa Indonesia. Pada penelitian ini, lexicon selain digunakan untuk pencarian kata dasar Bahasa Madura juga digunakan sebagai salah satu tahap pemberian POS Tagging. Hasil ujicoba dengan menggunakan lexicon mencapai akurasi yaitu 86.61% sedangkan jika tidak menggunakan lexicon hanya mencapai akurasi 28.95 %. Dari sini dapat disimpulkan bahwa ternyata lexicon sangat berpengaruh terhadap POS Tagging.

PERBANDINGAN METODE DOUBLE EXPONENTIAL SMOOTHNG DAN DOUBLE MOVING AVERAGE UNTUK PERAMALAN HARGA BERAS ECERAN DI KABUPATEN PAMEKASAN Indah Listiowarni; Nindian Puspa Dewi; Andrey Kartika Widhy Hapantenda
Jurnal Komputer Terapan Vol. 6 No. 2 (2020): Jurnal Komputer Terapan
Publisher : Politeknik Caltex Riau

Rice is the main carbohydrate source used by Indonesians as a staple food, so the availability and price are also a concern. The purpose of this study is to forecast monthly rice prices for 2019, while comparing 2 forecasting methods namely Double Moving Average and Double Exponential Smoothing to get the best forecasting results of rice prices. The data used in this study is retail rice prices from January 2011 to March 2019. Based on the tests conducted, the Double Moving Average method is better with the MAPE value reaching 0.582542%, and the MSE value reaching 6349.25 using the time order 3. Average monthly retail price forecasts for 2019 using the DMA method of Rp.12,169, -

Implementasi Pembelajaran E-Learning Pada Kelas Praktikum (Studi Kasus : Universitas Madura) Nindian Puspa Dewi; Nanik Winarsih
Jurnal Komputer Terapan Vol. 6 No. 2 (2020): Jurnal Komputer Terapan
Publisher : Politeknik Caltex Riau

E-learning is a container that handles online learning systems that can be accessed anywhere by utilizing internet access. Practicum is one of the activities to improve the understanding of theory by carrying out trials or exercises in accordance with the subjects taught, so that e-learning in question can be used as a container that can be used by practicum lecturers and students in handling the activities of prakitkum activities at the University Informatics Engineering Laboratory Madura In this research, an e-learning is made that can handle practical activities, such as managing assignments, making modules, conducting online exams, and discussions that are built using ionic and native php programming languages.

Implementasi Holt-Winters Exponential Smoothing untuk Peramalan Harga Bahan Pangan di Kabupaten Pamekasan Nindian Puspa Dewi; Indah Listiowarni
Digital Zone: Jurnal Teknologi Informasi dan Komunikasi Vol. 11 No. 2 (2020): Digital Zone: Jurnal Teknologi Informasi dan Komunikasi
Publisher : Publisher: Fakultas Ilmu Komputer, Institution: Universitas Lancang Kuning

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31849/digitalzone.v11i2.4797

Naik turunnya harga bahan pangan bisa menjadi penentu bagi setiap orang dalam menentukan makanan yang akan dikonsumsi, menyesuaikan dengan keadaan finansial mereka. Penelitian ini bertujuan untuk melakukan peramalan harga bahan pangan di masa mendatang dengan menggunakan data harga bahan pangan di masa sebelumnya. Dengan adanya peramalan harga, diharapkan dapat bermanfaat untuk membuat perencanaan pembelanjaan seperti perencanaan belanja bulanan dan penentuan harga jual makanan. Metode peramalan yang digunakan adalah Metode Holt-Winters Exponential Smoothing. Metode ini merupakan metode peramalan yang selain memperhatikan faktor trend juga melihat faktor musim. Penelitian ini hanya menggunakan harga bahan pangan di Kabupaten Pamekasan untuk periode 2012-2019. Hasil penelitian menunjukkan bahwa peramalan dengan menggunakan Metode Holt-Winters Exponential Smoothing menghasilkan nilai akurasi yang cukup baik dengan rata-rata nilai MAPE 1.2% untuk Model Multiplikatif dan 1.02% untuk Model Aditif. Hal ini menunjukkan bahwa Model Aditif lebih baik daripada Model Multiplikatif karena memiliki nilai MAPE yang lebih kecil. Kata kunci: holt-winters, penghalusan eksponensial, peramalan, harga, bahan pangan Abstract The fluctuation of food prices can be a determinant for everyone to choose what food they will consume, according to their financial condition. This study aims to forecast food prices in the future by using data on food prices in the past. With price forecasting, it can be useful for planning expenditures such as monthly shopping planning and determining the selling price of food. The method used in this research is the Holt-Winters Exponential Smoothing Method, which in addition to paying attention to trend factors, also observes season factors (seasonal). This study only uses food prices in Pamekasan Regency for the period 2012-2019. The results show that forecasting using the Holt-Winters Exponential Smoothing Method has a good accuracy value with an average MAPE value of 1.2% for the Multiplicative Model and 1.02% for the Additive Model. This result shows that Additive Model is better than Multiplicative Model. Keywords: holt-winters, exponential smoothing, forecasting, price, food.

Pemanfaatan Klasifikasi Soal Biologi Cognitive Domain Bloom’s Taxonomy Menggunakan KNN Chi-Square Sebagai Penyusunan Naskah Soal Indah Listiowarni; Nindian Puspa Dewi
Digital Zone: Jurnal Teknologi Informasi dan Komunikasi Vol. 11 No. 2 (2020): Digital Zone: Jurnal Teknologi Informasi dan Komunikasi
Publisher : Publisher: Fakultas Ilmu Komputer, Institution: Universitas Lancang Kuning

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31849/digitalzone.v11i2.4798

The question manuscript is a document that contains a collection of exam questions that are commonly used by an educator to test the absorption of their students on the material that has been presented in class. Question manuscripts made by educators are made based on a pre-made question grid, and contain a certain percentage of each cognitive domain category in the bloom taxonomy. The level in the bloom taxonomic cognitive domain describes the level of difficulty of each item made, so that an educator must first make a formula in a planning script called a question grid. The items that have been classified based on the cognitive domain taxonomic level of bloom using the KNN classifier method and the Chi-square feature selection are proven to be the right combination, the classification results of these items will be used for the preparation of a text for exam questions with an adjusted percentage formula. With the question grid that has been made beforehand, it is hoped that this research can be used to facilitate educators in drafting appropriate exam questions for their students

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