APLIKASI BERBASIS WEB PELAYANAN SURAT MENYURAT MENGGUNAKAN FITUR SMS GATEWAY (STUDI KASUS: DESA SUKAPURA)

Pelayanan surat menyurat di Desa Sukapura saat ini sudah menggunakan sebuah aplikasi berbasis desktop. Aplikasi tersebut hanya memiliki fungsionalitas untuk membuat surat saja sehingga masih banyak kekurangannya. Untuk itu dalam pembuatan aplikasi berbasis web pelayanan surat menyurat menggunakan fitur sms gateway memiliki fungsionalitas seperti pencatatan data permohonan surat beserta mengarsipkan persyaratannya, mencetak bukti atau resi surat sebagai tanda jika sudah melakukan permohonan, mengarsipkan surat yang telah dicetak dan ditanda tangani oleh kepala desa, membuat laporan surat yang telah dibuat, melihat progress surat yang dibuat, memberikan pengingat kepada pemohon jika surat yang dibuat telah selesai, menilai kinerja dalam proses pembuatan surat, dan mengagendakan surat masuk. Metode yang digunakan SDLC dengan model waterfall menggunakan bahasa pemrograman PHP dan javascript. Framework yang digunakan codeigniter dengan database MySQL dan pengujian menggunakan black box testing dan user acceptance testing (UAT). Sehingga diharapkan pelayanan surat menyurat di Desa Sukapura dapat berjalan dengan efisien dan efektif. Kata Kunci: Aplikasi, Surat, Waterfall, Sms Gateway, PH

Peningkatan Kinerja Smart Lighting dengan Activity Recognition Menggunakan Hierarchical Hidden Markov Model

Paper ini memiliki tujuan yaitu untuk meningkatkan kinerja smart lighting dengan activity recognition menggunakan hierarchical hidden markov model. Tujuan tersebut akan menjawab permasalahan yang terjadi yaitu lampu pintar hanya menyala jika manusia tepat berada dibawah lampunya sehingga diperlukannya lampu pintar yang di mana mampu membaca pergerakan orang saat mendekat ke lampu tersebut atau tidak. Kedua, ada juga lampu pintar tetapi saat manusia berada di bawah lampunya hanya menyala beberapa detik saja yang seharusnya menyala terus jika ada manusia di bawah atau daerah radius sekitar lampu sehingga diperlukannya lampu pintar yang mampu menyala saat ada orang di bawahnya dan akan off lampunya jika manusia tersebut berada di luar radius sekitar lampu. Model yang digunakan yaitu hierarchical hidden markov model yang merupakan perpanjangan dari hidden markov model yang dapat menyelesaikan masalah evaluasi, penarikan kesimpulan dan pembelajaran dengan algoritma yang digunakan yaitu algoritma viterbi. Hasil yang didapatkan menggunakan HHMM adalah akurasi 93%, recall 92% dan precision 86%, hasil tersebut didapatkan dari perhitungan melalui confusion matrix yang dimana confusion matrix diperoleh dari hasil pengujian yang dilakukan

A Proposed Hidden Markov Model Method for Dynamic Device Pairing on Internet of Things End-Devices

Dynamic device pairing is a context-based zero-interaction method to pair end-devices in an IoT System based on Received Signal Strength Indicator (RSSI) values. But if RSSI detection is done in high level, the accuracy is troublesome due to poor sampling rates. This research proposes the Hidden Markov Model method to increase the performance of dynamic device pairing detection. This research implements an IoT system consisting an Access Point, an IoT End Device, an IoT Platform, and an IoT application and performs a comparison of two different methods to prove the concept. The results show that the precision of dynamic device pairing with HMM is better than without HMM and the value is 83,93%

TEKNIK SMOTE DAN GINI SCORE DALAM KLASIFIKASI KANKER PAYUDARA

Breast cancer is a malignancy in breast tissue that can originate from the epithelium of the ducts and lobules. WHO says 30% - 50% of cancer cases can be prevented. Breast cancer prevention can be done utilizing screening or early diagnosis. The purpose of the initial diagnosis is that if a lump appears, predictions can be made whether it is classified as malignant or benign. Breast cancer prediction can be done using a dataset containing cancer-related parameters. However, sometimes the dataset used also has problems such as the amount of data is not balanced and the use of irrelevant features. This study aims to improve breast cancer prediction results by balancing the number of data classes and using the rank feature. The method used is SMOTE for imbalanced data and Gini score for rank features. The classification model used is random forest and naïve Bayes. The results obtained by the random forest classification model are superior to Naïve Bayes

Context-Aware Smart Door Lock with Activity Recognition Using Hierarchical Hidden Markov Model

Context-Aware Security demands a security system such as a Smart Door Lock to be flexible in determining security levels. The context can be in various forms; a person’s activity in the house is one of them and is proposed in this research. Several learning methods, such as Naïve Bayes, have been used previously to provide context-aware security systems, using related attributes. However conventional learning methods cannot be implemented directly to a Context-Aware system if the attribute of the learning process is low level. In the proposed system, attributes are in forms of movement data obtained from a PIR Sensor Network. Movement data is considered low level because it is not related directly to the desired context, which is activity. To solve the problem, the research proposes a hierarchical learning method, namely Hierarchical Hidden Markov Model (HHMM). HHMM will first transform the movement data into activity data through the first hierarchy, hence obtaining high level attributes through Activity Recognition. The second hierarchy will determine the security level through the activity pattern. To prove the success rate of the proposed method a comparison is made between HHMM, Naïve Bayes, and HMM. Through experiments created in a limited area with real sensed activity, the results show that HHMM provides a higher F1-Measure than Naïve Bayes and HMM in determining the desired context in the proposed system. Besides that, the accuracies obtained respectively are 88% compared to 75% and 82%

Pendekatan Deep Learning Untuk Prediksi Durasi Perjalanan

Setiap orang dalam kehidupan memiliki kecenderungan untuk berpindah dari satu tempat ke tempat lainnya. Perpindahan tersebut dapat dilakukan dengan menggunakan berbagai macam cara seperti menggunakan transportasi pribadi atau umum (bus, taksi, pesawat, dan kereta api), Pada perkembangan teknologi saat ini mode transportasi sudah semakin canggih. Akan tetapi masih ada mode transportasi yang belum modern misalnya seperti taksi, dimana salah satunya tidak dapat memprediksi lama waktu perjalanan. Meskipun sudah ada taksi yang berbasis online seperti Uber, akan tetapi masih banyak taksi yang belum berbasis online sehingga tidak bisa dilakukan estimasi waktu dan jarak. Permasalahan di atas dapat diselesaikan dengan cara melakukan pendekatan berbasis pembelajaran mesin. Salah satu keuntungan yang didapatkan jika kita dapat mengetahui lama waktu estimasi perjalanan yaitu dapat mengatur waktu perjalanan sesuai dengan rutinitas yang sedang dikerjakan ataupun juga dapat menghemat biaya yang dikeluarkan dengan mengetahui jarak yang akan dijalankan. Pada penelitian ini bertujuan untuk memprediksi durasi perjalanan pada dataset New York taxi trip duration menggunakan pendekatan deep learning yaitu Long Short Term Memory Reccurent Neural Network (LSTM-RNN). Eksperimen dilakukan dengan melakukan tuning parameter terkait seperti epoch, nilai dropout, dan neurons. Pengukuran hasil menggunakan nilai Root Mean Square Error (RMSE) dan nilai loss. Hasil yang didapatkan menggunakan model LSTM-RNN sebesar 0,0012 untuk nilai loss dan RMSE 0,4

Implementasi Website Rahayu River Tubing sebagai Media Promosi dan Reservasi bagi Wisatawan

Community service aims to implement website-based information technology as a media for promoting Rahayu River Tubing tourism and its reservation. The attractions offered are Tubing, Pangasinan Waterfall, Pengkol Hill, and Pedestal View. Before this website, promotions were only carried out on social media, and reservations were still manual by contacting the manager directly. This community service method is by identifying problems, developing the Rahayu River Tubing website at the address http://rahayurivertubing.com, and evaluating activities. This activity was attended by 15 participants from the manager and tour guide held at the Rahayu River Tubing basecamp. At the end of the activity, feedback is given to evaluate this community service activity. There are five statements on the questionnaire with an average result of 99%. These results indicate that community service partners are delighted with the socialization and training on the use of the Rahayu River Tubing tourism website

Implementasi Website Rahayu River Tubing sebagai Media Promosi dan Reservasi bagi Wisatawan

Community service aims to implement website-based information technology as a media for promoting Rahayu River Tubing tourism and its reservation. The attractions offered are Tubing, Pangasinan Waterfall, Pengkol Hill, and Pedestal View. Before this website, promotions were only carried out on social media, and reservations were still manual by contacting the manager directly. This community service method is by identifying problems, developing the Rahayu River Tubing website at the address http://rahayurivertubing.com, and evaluating activities. This activity was attended by 15 participants from the manager and tour guide held at the Rahayu River Tubing basecamp. At the end of the activity, feedback is given to evaluate this community service activity. There are five statements on the questionnaire with an average result of 99%. These results indicate that community service partners are delighted with the socialization and training on the use of the Rahayu River Tubing tourism website

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JURNAL INFOTE