Abstract:Termites are the most destructive pests and their attacks significantly impact the quality of wooden buildings. Due to their cryptic behavior, it is rarely apparent from visual observation that a termite infestation is active and that wood damage is occurring. Based on the phenomenon of acoustic signals generated by termites when attacking wood, we proposed a practical framework to detect termites nondestructively, i.e., by using the acoustic signals extraction. This method has the pros to maintain the quality of wood products and prevent higher termite attacks. In this work, we inserted 220 subterranean termites into a pine wood for feeding activity and monitored its acoustic signal. The two acoustic features (i.e., energy and entropy) derived from the time domain were used for this study's analysis. Furthermore, the support vector machine (SVM) algorithm with different kernel functions (i.e., linear, radial basis function, sigmoid and polynomial) were employed to recognize the termites' acoustic signal. In addition, the area under a receiver operating characteristic curve (AUC) was also adopted to analyze and improve the performance results. Based on the numerical analysis, the SVM with polynomial kernel function achieves the best classification accuracy of 0.9188.

As a pioneer in e-learning implementation, UT is required to be able to provide an adequate information technology system continuously, particularly to reach an extensive area of Indonesia. The purpose of this study was to asses of e-learning implementation and to evaluate the factors that influenced e-learning at UT using the DeLone and McLean information system models. Data was collected using a structured questionnaire among two respondent categories, namely students and lecturers, using a convenience sampling method. The data from 300 respondents were then analyzed using the Partial Least Square method. The results indicate that information quality, system quality, service quality positive significantly influenced user satisfaction. Thus, information quality and service quality positive significantly influenced usage. The usage positive significantly influenced user experience, and the usage and user experience positive significantly influenced the net benefit. The conclusion was UT had implemented comprehensive e-learning with high success rates. This success was from the technical aspects of system quality and service quality. Effectively, this success also came from system usage, user satisfaction, and net results. The need for research Implications was explored for a suggestion for better management and utilization of e-learning strategies at the Indonesia Open University.Abstrak: UT sebagai pionir perguruan tinggi yang menerapkan e-learning dituntut selalu dapat menyediakan sistem teknologi informasi yang memadai untuk menjangkau luasnya wilayah Indonesia. Tujuan dari penelitian ini adalah menilai kondisi penerapan e-learning dan menganalisis faktor-faktor yang memengaruhi keberhasilannya dengan menggunakan model sistem informasi DeLone dan McLean. Data dikumpulkan melalui kuesioner terstruktur dengan mahasiswa dan dosen menjadi responden penelitian. Pengumpulan sampel dilakukan dengan metode convenience sampling. Data dari 300 responden kemudian dianalisis dengan Partial Least Square. Hasilnya adalah kualitas informasi, kualitas sistem, dan kualitas layanan berpengaruh positif terhadap kepuasan pengguna. Kualitas layanan dan kualitas informasi berpengaruh positif terhadap penggunaan. Penggunaan berpengaruh positif terhadap kepuasan pengguna, dan penggunaan serta kepuasan pengguna berpengaruh positif terhadap hasil bersih. Kesimpulan yang diperoleh adalah UT telah mengimplementasikan e-learning secara menyeluruh dengan tingkat keberhasilan tinggi. Keberhasilan tersebut dilihat secara teknis yang tercermin dari kualitas sistem dan kualitas layanan. Secara efektifitas, keberhasilan tercermin dari penggunaan sistem, kepuasan pengguna, dan hasil bersih. Beberapa implikasi dari penelitian ini kemudian disajikan sebagai usulan bagi strategi pengelolaan dan pemanfaatan e-learning di UT yang lebih baik lagi.Kata kunci: DeLone dan McLean, e-learning, PLS, sistem informasi, Universitas Terbuka

Various methods for termite detection have been developed, one of which is purely based on their acoustic signals. However, this method has a weakness, as it is difficult to separate the signals generated by the termites from noise in the environment. A combination of the feature extraction of the acoustic signals and a classification model is expected to overcome this weakness. In this investigation, we inserted 220 subterranean termites Coptotermes curvignathus into pine wood for feeding activity and observed their acoustic signals. In addition, three acoustic features (shortterm energy, entropy and zero moment power) were proposed to recognize the termites' acoustic signals. Subsequently, these features were analyzed and combined with discriminant analysis to produce a robust classification model. According to the numerical results, the integrated discriminant analysis and the acoustic feature in our termite detection system has an accuracy of 83.75%.

<p>ABSTRAK<br />Jarak pagar berpotensi sebagai sumber biodiesel karena kandungan<br />lemak yang tinggi (&gt;40%) dan belum ada penggunaan lainnya.<br />Spektroskopi (Near Infrared) NIR adalah metode yang cepat untuk<br />mengukur spektrum sampel dan tidak terdapat limbah kimia. Tujuan<br />penelitian adalah mengembangkan metode pendugaan komposisi kimia<br />beberapa  provenan  jarak  pagar  berdasarkan  spektroskopi  NIR<br />menggunakan kalibrasi PLS. Pengujian dilakukan menggunakan tiga<br />provenan jarak pagar yaitu IP-3A, IP-3M, dan IP-3P masing-masing 85<br />sampel. Spektrum reflektansi diukur menggunakan alat NIRFlex Solids<br />Petri pada panjang gelombang 1000–2500 nm. Sekitar ⅔ jumlah sampel<br />digunakan untuk mengembangkan persamaan kalibrasi dan ⅓ jumlah<br />sampel untuk validasi. Pra perlakuan data spektrum dilakukan dengan<br />normalisasi antara 0-1, turunan pertama Savitzky-Golay 9 titik dan<br />gabungan keduanya. Hasil penelitian menunjukkan spektroskopi NIR<br />dapat menduga kadar air, lemak, dan asam lemak bebas . Koefisien<br />korelasi (r) antara komponen kimia metode acuan dengan dugaan NIR<br />&gt;0,83 menunjukkan ketepatan model cukup baik (r kadar air=0,96, r kadar<br />lemak=0,92, dan r ALB=0,89 ). Konsistensi model kalibrasi kadar<br />air=94,85%, lemak=82,56%, dan ALB=87,80%. Koefisien keragaman<br />dugaan (Prediction Coeficient Variability/PCV) ketiga model &lt;10%<br />menunjukkan model yang dibangun cukup handal. Ratio of standard error<br />prediction to deviation (RPD) menunjukkan metode spektroskopi NIR<br />dapat digunakan untuk menentukan kadar air (RPD=3,30) dan lemak<br />(RPD=2,06). Model-model yang dikembangkan secara umum layak<br />untuk menentukan kadar air dan lemak biji jarak pagar, tetapi belum<br />optimal untuk penentuan kadar ALB biji jarak pagar.<br />Kata kunci: NIR , jarak pagar, kadar air, kadar lemak, kadar asam lemak<br />bebas</p><p>ABSTRACT<br />Physic nut is a potential source of biodiesel. It is high in fat content,<br />above 40% and has not been usesed for other purposes. Moisture, free fatty<br />acid, and fat content are the chemical compounds and determinant factor<br />for physic nut seed quality. The objective of this study was to develop a<br />method to predict chemical composition of physic nut by NIR<br />spectroscopy and PLS calibration. The study was conducted using three<br />provenances of physic nut, i.e. IP-3A, IP-3M, and IP-3P, with 85 samples<br />each. The wavelengths of near infrared reflectance ranged from 1000 to<br />2500 nm, and measured by NIR Flex Solids Petri Apparatus.<br />Approximately ⅔ of total samples were used for developing calibration<br />equation, while ⅓ of total samples for performing validation. Pre-treatment<br />of spectrum data was done by applying normalization, first derivative of<br />Savitzky–Golay 9 points, and as well as their combination. The results<br />showed that NIR spectroscopy performed acceptable prediction for<br />moisture and fat content. Correlation coefficients (r) between the reference<br />method and NIR prediction were 0.96 for moisture content, 0.92 for fat<br />content, and 0.89 for FFA and the consistency of the model were 94.85%<br />for moisture content, 82.56% for fat, and 87.80% for FFA. Prediction of<br />coefficient of variability (PCV) of the three models ≤10 % shows that the<br />models are reliable. Ratio of standard error prediction to deviation (RPD)<br />for moisture content has the potential to be used for screening (RPD=3.30)<br />though the fat content model has rough screening (RPD=2.06).<br />Key words: NIR, physic nut, moisture, fat, free fatty acid contents.</p>

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