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Implementation of Mel Frequency Cepstral Coefficient and Dynamic Time Warping For Bird Sound Classification Prapcoyo, Hari; Adhita Putra, Bertha Pratama; Perwira, Rifki Indra
SENATIK STT Adisutjipto Vol 5 (2019): Peran Teknologi untuk Revitalisasi Bandara dan Transportasi Udara [ISBN XXX-XXX-XXXXX-
Publisher : Sekolah Tinggi Teknologi Adisutjipto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28989/senatik.v5i0.326

Lovebird (Agapornis) is a type of bird that has become the belle of new pet birds lately. The interest of the hobbyist in this one song is because Lovebird has a unique chirp. For beginner lovebird fans, the lack of knowledge and experience about lovebird birds results in various cases of fraud in choosing a quality lovebird. They were disappointed expensive lovebirds that had been purchased but did not match what was expected.Lovebird chirping voice recognition can be learned and recognized through the learning process of speaker recognition, which is part of voice recognition. Speaker recognition captures the frequency of the lovebird's voice, then compares it with the sound frequency of the existing training data. The sound frequency and the long duration of chirping of lovebird birds will be extracted through the Mel-Frequency Cepstral Coefficient (MFCC) method. Information in the form of Mel Frequency Cepstrum Coefficients from input data and training data is then compared to the Dynamic Time Warping method. The methodology used in this study uses the grapple method.The results of this study were obtained an accuracy value of sound validation by 80%. It is hoped that with the capabilities of this system, it can help bird chirping lovers know the sound quality of lovebird birds that are good, moderate, and less. Also, it can help the jury of birds chirping, so that it can be used as an accurate standard in classifying lovebird sounds.

ANALISIS PENERIMAAN DAN KEPUASAN PENGGUNA WEB UPNYK BAGI MAHASISWA SISTEM INFORMASI SEMESTER 1-4 MENGGUNAKAN TECHNOLOGY ACCEPTANCE MODEL (TAM) DAN PARTIAL LEAST SQUARE (PLS) Hari Prapcoyo; Mohamad As’ad; Frans Richard Kodong
Seminar Nasional Informatika (SEMNASIF) Vol 1, No 1 (2018): Landscape Industri Internet Dampak Perilaku Marketing Indonesia
Publisher : Jurusan Teknik Informatika

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

Website upnyk.ac.id dibangun untuk memberikan kemudahan dan informasi kepada mahasiswa dan masyarakat umum. Penelitian ini bertujuan untuk mengevaluasi terhadap penerimaan dan kepuasan mahasiswa Sistem Informasi (SI) semester 1-4 terhadap website dengan pendekatan Technology Acceptance Model (TAM) dan Partial Least Square(PLS). Sampel mahasiswa sejumlah 64 responden dengan 27 pertanyaan. Variabel yang digunakan dalam TAM ada 5 variabel yaitu persepsi kegunaan(PU), persepsi kemudahan dalam menggunakan website(PEU), persepsi keinginan pengguna(BI), persepsi sikap pengguna(ATU) dan persepsi kualitas website(PWQ). Data dianalisis dengan model PLS, tahap awal adalah menentukan outer model dengan melihat loading faktor harus diatas 0,5. Ada tiga indikator variabel yang didroping yaitu pertanyaan ke 18 pada variabel ATU, pertanyaan ke 26 dan 27 pada variabel BI. Setelah droping ketiga indikator variabel, nilai average variance extracted(AVE) diatas 0,5. Nilai composite reliability diatas 0,7. Ada 7 hipotesis dalam penelitian ini; H1:PEU berpengaruh terhadap ATU;H2:PEU berpengaruh terhadap PU;H3:PU berpengaruh terhadap ATU;H4:ATU berpengaruh terhadap BI;H5:ATU berpengaruh terhadap BI;H6:PU berpengaruh terhadap BI; H7:BI berpengaruh terhadap PWQ. Dari tujuh hipotesis tersebut, yang tidak signifikant adalah hipotesis ke tiga(H3). Dari model tersebut didapat nilai Q-Square sebesar 90,53 %, ini berarti PWQ dapat dijelaskan oleh variabel PU, PEU, ATU dan BI sebesar 90,53%. Saran dari penelitian ini kepada pengelola website upnyk adalah tampilan visual website kurang signifikan, sehingga mahasiswa kurang merekomendasikan kepada pihak lain. Selain itu mahasiswa tidak respon terhadap perbaikan website kemungkinan tidak ada sarana untuk memberikan saran perbaikan website.

IMPLEMENTASI APLIKASI ANDROID-BASED UNTUK PENANGGULANGAN WELL KICK PADA PEMBORAN MINYAK DAN GAS BUMI Frans Richard Kodong; Herry Sofyan; Hari Prapcoyo
Seminar Nasional Informatika (SEMNASIF) Vol 1, No 1 (2020): Peran Digital Society dalam Pemulihan Pasca Pandemi
Publisher : Jurusan Teknik Informatika

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AbstractWell kick is one of the knowledge that workers in oil and gas drilling and geothermal drilling operations should know to prevent blow out. Kick is a condition where the formation fluid has entered the well hole, where the fluid will push the contents of the hole to the surface which is called blow out. Well kick control in drilling operations is very important, because uncontrolled bursts will cause many major problems for the company. In well kick countermeasures a lot of data has to be recorded and computed to analyze the kicks taken and based on this calculation it is usually possible to decide on the proper method of dealing with the kick so that there is no explosion. Many calculations are done manually, which take a long time and the calculations may not be accurate or precise. In addition, the applications or software used in the field to calculate Well Kick countermeasures are very expensive. To help Driller perform these calculations, this research will develop an Android-based Well Kick countermeasure application that is economical and flexible so that it is easy to carry when handling Well Kick which occurs in the field based on an Android Smartphone.Keywords: Kick, Smartphone, Android, PreventionWell kick merupakan salah satu ilmu yang harus diketahui oleh para pekerja di operasi pengeboran minyak dan gas serta pengeboran panas bumi untuk mencegah terjadinya blow out. Kick adalah suatu kondisi dimana fluida formasi telah masuk ke dalam lubang sumur, dimana fluida tersebut akan mendorong isi lubang ke permukaan yang disebut dengan blow out. Pengendalian well kick dalam operasi pemboran sangat penting, karena semburan yang tidak terkendali akan menimbulkan banyak masalah besar bagi perusahaan, seperti biaya tinggi, korban jiwa, kerusakan lingkungan, dan berkurangnya potensi cadangan terutama di bawah bumi. Dalam penanggulangan tendangan sumur, banyak data harus dicatat dan dihitung untuk menganalisis tendangan yang terjadi dan berdasarkan perhitungan ini biasanya mungkin untuk memutuskan metode yang tepat untuk menangani tendangan sehingga tidak terjadi ledakan. Banyak kalkulasi yang dilakukan secara manual, yang memakan waktu lama dan kalkulasi yang dilakukan belum tentu akurat atau tepat. Selain itu, aplikasi atau software yang digunakan di lapangan dalam menghitung tindakan penanggulangan Well Kick sangatlah mahal. Untuk membantu Driller melakukan perhitungan tersebut maka penelitian ini akan mengembangkan aplikasi penanggulangan Well Kick berbasis Android yang ekonomis dan fleksibel sehingga mudah dibawa saat menangani Well Kick yang terjadi di lapangan berbasis Smartphone Android.Kata Kunci : Kick, Smartphone, Android, Prevention

PERAMALAN JUMLAH MAHASISWA MENGGUNAKAN MOVING AVERAGE Hari Prapcoyo
Telematika Vol 15, No 1 (2018): Edisi April 2018
Publisher : Jurusan Teknik Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31315/telematika.v15i1.3069

AbstractThe Process of using resources in higher education is influenced by the up and down of the number students. The purpose of this study is to predict the number of students who study in the department of informatics engineering UPN Veteran Yogyakarta for the next periods. This research, data is taken from forlap dikti for Informatics Engineering fom 2009 until 2016 at UPN Veteran Yogyakarta. The method that used to forecast the number of students is a Moving Average method consisting of: Single Moving Average (SMA), Weighted Moving Average (WMA) and Exponential Moving Average (EMA). This study will use the forecasting accuracy namely Mean Square Error (MSE), Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE) to select the best model to be used for forecasting. The best model that used for forecasting is Weighted Moving Average (WMA) with weighted 1/3 and average length (n) used for 2. The smallest value for MSE of 5807.96; the smallest MAE value of 55.89 and the smallest value for MAPE of 5.24%. Forecasting of the number of students for four semesters in the future after the even semester of 2016 are respectively: 902; 901,33; 901,56 and 901,48. Keywords : Forecasting, UPN Veteran Yogyakarta, Single moving average(SMA) AbstrakProses penggunaan sumber daya perguruan tinggi setiap tahun dipengaruhi oleh naik turunnya jumlah mahasiswa. Tujuan dari penelitian ini adalah untuk memprediksi jumlah mahasiswa yang kuliah di jurusan teknik informatika UPN Veteran Yogyakarta untuk periode yang akan datang. Data penelitian ini diambil dari forlap dikti untuk Teknik Informatika dari tahun 2009 sampai 2016 UPN Veteran Yogyakarta. Metode yang digunakan untuk melakukan peramalan jumlah mahasiswa adalah metode Moving Average yang tediri dari : Single Moving Average (SMA), Weighted Moving Average (WMA) dan Exponential Moving Average (EMA). Penelitian ini akan menggunkan akurasi peramalan Mean Square Error (MSE), Mean Absolute Error (MAE) dan Mean Absolute Percentage Error (MAPE) untuk memilih model terbaik yang akan digunakan untuk peramalan. Model terbaik yang digunakan untuk peramalan yaitu Weighted Moving Average (WMA) dengan pembobot 1/3 dan panjang rata-rata (n) yang dipakai sebesar 2. Nilai terkecil untuk MSE sebesar 5807,96; nilai terkecil MAE sebesar 55,89 dan nilai terkecil untuk MAPE sebesar 5,24 %. Peramalan untuk jumlah mahasiswa empat semester kedepan setelah semester genap 2016 masing-masing adalah : 902; 901,33; 901,56 dan 901,48. Kata Kunci : Peramalan, UPN Veteran Yogyakarta, Single Moving Average(SMA).

THE FORECASTING OF MONTHLY INFLATION IN YOGYAKARTA CITY USES AN EXPONENTIAL SMOOTHING-STATE SPACE MODEL Hari Prapcoyo; Mohamad As'ad
International Journal of Economics, Business and Accounting Research (IJEBAR) Vol 6, No 2 (2022): IJEBAR, VOL. 06 ISSUE 02, JUNE 2022
Publisher : LPPM ITB AAS INDONESIA (d.h STIE AAS Surakarta)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29040/ijebar.v6i2.4853

Abstract: Yogyakarta is known as a student city, tourist city, and also a city of culture. Yogyakarta is an interesting tourist and cultural place with many beautiful tourist attractions in the city of Yogyakarta. Public transportation in the city of Yogyakarta is also varied, ranging from conventional and online-based. Access to the city of Yogyakarta varies, namely trains, buses, and planes. Thus, the economic growth in the city of Yogyakarta is getting better, this can be seen from the economic activity in the city of Yogyakarta which is getting busier. A good economy is usually always followed by stable inflation. This study aims to predict inflation in the future period using the Exponential Smoothing-State Space (ETS) model. Secondary monthly inflation data was obtained from BPS Yogyakarta City. From this research, the Exponential Smoothing-State Space Model / ETS (A, N, A) is obtained, which means that the monthly inflation data for the city of Yogyakarta does not contain trends, but contains additive seasonality and has additive errors. The results of this study indicate that inflation in the next three months is relatively stable, namely, the decline in inflation and the increase in inflation is still below 10%. Keywords: BPS Yogyakarta City, Monthly Inflation Forecasting, Exponential Smoothing-State Space ETS

Model Neural Network Autoregressive untuk Prediksi Inflasi Bulanan di Kota Yogyakarta Hari Prapcoyo; Mohamad As'ad
JUSTIN (Jurnal Sistem dan Teknologi Informasi) Vol 11, No 2 (2023)
Publisher : Jurusan Informatika Universitas Tanjungpura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26418/justin.v11i2.54370

AbstrakYogyakarta sebagai kota pelajar, kota pariwisata ataupun kota budaya sangatlah ramai aktifitas ekonominya karena banyak sekolah, universitas, tempat wisata dan juga tempat budaya yang tentunya banyak mahasiswa, wisatawan dalam negeri maupun luar negeri yang berkunjung ke kota tersebut. Aktifitas mahasiswa dan wisatawan di kota Yogyakarta ini bisa meningkatkan aktifitas perekonomian seperti tempat kost, penginapan atau hotel serta tidak ketinggalan tempat makan, tempat belanja dan lain sebagainya. Penelitian ini mempunyai tujuan untuk memprediksi inflasi bulanan di kota Yogyakarta yang ramai tersebut. Data sekunder inflasi bulanan untuk kota Yogyakarta diperoleh dari BPS kota Yogyakarta dan BPS pusat. Â Data yang digunakan yaitu data inflasi bulanan mulai dari Januari 2006 sampai dengan Desember 2021, sebanyak 192 data. Penelitian ini menggunakan model peramalan jaringan syaraf tiruan (JST) atau artificial neural network (ANN). Model JST atau ANN yang digunakan yaitu model neural network autoregressive (NNAR). Model NNAR ini menggunakan algoritma backpropogation dengan fungsi aktifasi sigmoid biner. Pengolahan data pada penelitian ini menggunakan R package statistics yang merupakan open source program. Hasil kesimpulan dari penelitian ini adalah diperoleh model terbaik yaitu NNAR(12,8) artinyaÂ model NNAR ini mempunyai input berupa lag-1 sampai dengan lag-12 inflasi bulanan koya Yogyakarta dengan single hiden layer mempunyai 8 neuron. Akurasi model NNAR(12,8) di ukur dengan root mean square error (RMSE, sebesar 0.05962758), mean absolute square error (MASE, sebesar 0.1011443), mean absolute percentage error (MAPE, sebesar 28.32449). Saran dari penelitian ini untuk penelitian lanjutan, model NNAR(12,8) hendaknya dibandingkan dengan model ANN yang lain atau model yang berbasis sistem cerdas (artificial intelegent, AI).

Forecasting Performance of Double Exponential Smoothing Model and ETS Model for Predicting Crude Oil Prices Hari Prapcoyo; Mohamad As'ad; Sujito Sujito; Sigit Setyowibowo; Eni Farida
Telematika Vol 20, No 2 (2023): Edisi Juni 2023
Publisher : Jurusan Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31315/telematika.v20i2.8104

Purpose: This study aims to predict the price of monthly crude oil quickly and accurately by using an easy model and with easily available software.Design/methodology/approach: This study compares the DES-Holts and ETS models to predict price of monthly crude oil.Findings/result: The results of this study recommend the ETS(M,N,N) model to predict the price of monthly crude oil which produces an accuracy value of RMSE and MAPE of 4.385812 and 6.499007 %, respectively.Originality/value/state of the art: This study implements the DES_Holt's and ETS models to predict price of monthly crude oil with an RMSE and MAPE forecasting accuracy that has never been done in previous studies.

Preprocessing Using SMOTE and K-Means for Classification by Logistic Regression on Pima Indian Diabetes Dataset Ahmad Taufiq Akbar; Rochmat Husaini; Hari Prapcoyo
Telematika Vol 20, No 2 (2023): Edisi Juni 2023
Publisher : Jurusan Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31315/telematika.v20i2.9676

Purpose: Our study aims to combine pre-processing methods to develop a training data model from the Indian diabetic Pima dataset so that it can improve the performance of machine learning in recognizing diabetesDesign/methodology/approach: This research was started through several stages such as collecting the Pima indian diabetes dataset, pre-processing including k-means clustering, oversampling using SMOTE, then undersampling the dataset whose cluster is a minority in each class. Furthermore, the dataset is classified using machine learning namely logistic regression through 10 cross validationFindings/result: The results of this classification performance show that the accuracy reaches 99.5% and is higher than the method in previous studies.Originality/value/state of the art:The method in this study uses SMOTE to handle data imbalances and k-means clustering to remove outliers by removing labels that do not match the majority cluster in each class so that clean data is produced and validation using logistic regression is more accurate than previous studies.Tujuan: Penelitian ini bertujuan untuk menerapkan metode pre-processing untuk membentuk model data latih dari dataset Pima Indian diabetes sehingga dapat meningkatkan performa mesin pembelajaran dalam mengenali diabetes.Perancangan/metode/pendekatan: Riset ini dimulai melalui beberapa tahap yakni pengumpulan dataset Pima Indian diabetes, pre-processing meliputi clustering, oversampling menggunakan SMOTE, kemudian undersampling pada dataset pada klaster minoritas pada setiap kelas. Selanjutnya dataset diklasifikasikan menggunakan machine learning yakni metode regresi logistik melalui 10 cross validationHasil: Hasil dari performa klasifikasi ini menunjukkan akurasi mencapai 99,5% dan lebih tinggi daripada metode pada penelitian sebelumnya.Keaslian/ state of the art: Metode dalam penelitian ini menggunakan SMOTE untuk menangani ketidakseimbangan data dan k-means klastering untuk membuang outlier dengan cara menghapus label yang tidak sesuai dengan klaster mayoritas pada setiap kelas sehingga dihasilkan data yang bersih dan pada validasi menggunakan logistic regression lebih akurat daripada penelitian sebelumnya.

THE FORECASTING OF MONTHLY INFLATION IN YOGYAKARTA CITY USES AN EXPONENTIAL SMOOTHING-STATE SPACE MODEL Hari Prapcoyo; Mohamad As'ad
International Journal of Economics, Business and Accounting Research (IJEBAR) Vol 6, No 2 (2022): IJEBAR, Vol. 6 Issue 2, June 2022
Publisher : LPPM ITB AAS INDONESIA (d.h STIE AAS Surakarta)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29040/ijebar.v6i2.4853

Abstract: Yogyakarta is known as a student city, tourist city, and also a city of culture. Yogyakarta is an interesting tourist and cultural place with many beautiful tourist attractions in the city of Yogyakarta. Public transportation in the city of Yogyakarta is also varied, ranging from conventional and online-based. Access to the city of Yogyakarta varies, namely trains, buses, and planes. Thus, the economic growth in the city of Yogyakarta is getting better, this can be seen from the economic activity in the city of Yogyakarta which is getting busier. A good economy is usually always followed by stable inflation. This study aims to predict inflation in the future period using the Exponential Smoothing-State Space (ETS) model. Secondary monthly inflation data was obtained from BPS Yogyakarta City. From this research, the Exponential Smoothing-State Space Model / ETS (A, N, A) is obtained, which means that the monthly inflation data for the city of Yogyakarta does not contain trends, but contains additive seasonality and has additive errors. The results of this study indicate that inflation in the next three months is relatively stable, namely, the decline in inflation and the increase in inflation is still below 10%. Keywords: BPS Yogyakarta City, Monthly Inflation Forecasting, Exponential Smoothing-State Space ETS

The Single Sign On Model Using SAML and OAuth for Online Application of UPNYK Ahmad Taufiq Akbar; Hari Prapcoyo; Rifki Indra Perwira
JELIKU (Jurnal Elektronik Ilmu Komputer Udayana) Vol 12 No 4 (2024): JELIKU Volume 12 No 4, May 2024
Publisher : Informatics Department, Faculty of Mathematics and Natural Sciences, Udayana University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/JLK.2024.v12.i04.p19

Big companies have different systems both in terms of applications as well as the operating system, which requires each user to login to each different applications over and over again. With the SSO, users only need to remember one username and one password, but apply automatically universal across enterprise applications, so in this way it can be easier by using SAML (Security Assertion Markup language) for applications to be integrated without having to create a separate user validation. This SAML technology is an XML-based framework and can guarantee the encryption of all or part of the data and then convey it to the end user. Moreover, it allows easy and secure data exchange between systems. The data exchange will be protected by authorization and authentication through tokens containing statements to pass data between users authorized by SAML. SAML can be supported by OAUTH as bearer protocol to provide extensive security when user access services along side on the SSO network

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